Systems and Methods for Detecting Impairment Based Upon Movement Data

ABSTRACT

Various embodiments provide systems and methods for identifying impairment using measurement devices and trained models, and/or for indicating interference with impairment testing. Embodiments discussed herein provide systems, methods, and/or devices that enable remote impairment testing that does not require a human monitor to be present or physically near the individual being monitored. Such an ability is an improvement.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to (i.e., is a non-provisionalof) U.S. Pat. App. No. 63/389,258 entitled “Systems and Methods forDetecting Alcohol Sensor Interference”, and filed Jul. 14, 2022 byMiller; U.S. Pat. App. No. 63/349,496 entitled “Systems and Methods ForDetect Drug Use Via Breath Sample with Remote Biometric”, and filed Jun.6, 2022 by Miller et al; U.S. Pat. App. No. 63/393,498 entitled “Systemsand Methods for Learning and Classifying VOCs in Breath”, and filed Jul.29, 2022 by Miller; U.S. Pat. App. No. 63/393,505 entitled “Systems andMethods for Classifying Voice Slurring”, and filed Jul. 29, 2022 byMiller; U.S. Pat. App. No. 63/393,513 entitled “Systems and Methods forLearning and Classifying User Movement”, and filed Jul. 29, 2022 byMiller; and U.S. Pat. App. No. 63/393,519 entitled “Systems and MethodsLearning and Classifying Facial Expressions”, and filed Jul. 29, 2022 byMiller. The entirety of each of the aforementioned references areincorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

Various embodiments provide systems and methods for identifyingimpairment using measurement devices and trained models, and/or forindicating interference with impairment testing.

Large numbers of individuals are currently monitored as part of parolerequirements or other requirements. Such monitoring allows a monitoringagency to determine whether the individual is engaging in acceptablepatterns of behavior, and where an unacceptable behavior is identifiedto stop such behavior going forward. It is common to obtain samples froman individual to prove or disprove use of drugs or alcohol. It is alsocommon for an individual to attempt to defeat such testing.

Thus, for at least the aforementioned reasons, there exists a need inthe art for more advanced approaches, devices and systems for monitoringpotential impairment of individuals.

BRIEF SUMMARY OF THE INVENTION

Various embodiments provide systems and methods for identifyingimpairment using measurement devices and trained models, and/or forindicating interference with impairment testing.

This summary provides only a general outline of some embodiments. Manyother objects, features, advantages and other embodiments will becomemore fully apparent from the following detailed description, theappended claims and the accompanying drawings and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the various embodiments may be realized byreference to the figures which are described in remaining portions ofthe specification. In the figures, similar reference numerals are usedthroughout several drawings to refer to similar components. In someinstances, a sub-label consisting of a lower case letter is associatedwith a reference numeral to denote one of multiple similar components.When reference is made to a reference numeral without specification toan existing sub-label, it is intended to refer to all such multiplesimilar components.

FIG. 1A shows a scenario where a monitored individual is using animpairment detection device while an image is taken of the monitoredindividual using the impairment detection device;

FIG. 1B is a block diagram of a user detached monitor device includingvarious sensors and processors usable in accordance with one or moreembodiments;

FIG. 1C is a block diagram of an impairment detection device capable ofreceiving input from a monitored individual and generating an impairmentresult based upon the input from the monitored individual andtransferring the impairment result to a central monitor via atransceiver that is usable in relation to various embodiments;

FIG. 1D is a block diagram of a central monitoring system capable ofcommunicating with one or both of a user detached monitoring deviceand/or an impairment detection device and performing multi-predictorimpairment classification in accordance with various embodiments;

FIG. 1E is a block diagram of a central monitoring system capable ofcommunicating with one or both of a user detached monitoring deviceand/or an impairment detection device and performing multi-predictorimpairment classification and/or individual class impairmentclassification in accordance with other embodiments;

FIG. 2 is a flow diagram showing a method in accordance with someembodiments for configuring either a user detached monitor device or abreath based impairment detection device to perform impairment detectionusing one or more trained models;

FIG. 3 is a flow diagram in accordance with some embodiments showing amethod for training an interference classification model based at leastin part upon newly received interference images;

FIG. 4 is a flow diagram in accordance with some embodiments showing amethod for training a drug impairment model based at least in part uponnewly received drug impairment data;

FIG. 5 is a flow diagram in accordance with some embodiments showing amethod for training a facial image based impairment model based at leastin part upon newly received facial image data;

FIG. 6 is a flow diagram in accordance with various embodiments showinga method for maintaining a standard user facial image database updatedwith newly received facial images classified as non-impaired;

FIG. 7 is a flow diagram in accordance with some embodiments showing amethod for training a movement based impairment model based at least inpart upon newly received movement data;

FIG. 8 is a flow diagram in accordance with various embodiments showinga method for maintaining a standard user movement database updated withnewly received movement data classified as non-impaired;

FIG. 9 is a flow diagram in accordance with some embodiments showing amethod for training a voice data based impairment model based at leastin part upon newly received voice data;

FIG. 10 is a flow diagram in accordance with various embodiments showinga method for maintaining a standard user voice database updated withnewly received voice data classified as non-impaired;

FIG. 11 is a flow diagram showing a method in accordance with someembodiments for determining impairment using selectively appliedimpairment models and processes;

FIG. 12 is a flow diagram showing a method in accordance with variousembodiments for determining impairment based upon breath alcoholmeasurements;

FIGS. 13 a-13 b show example images used in relation to some embodimentsfor determining interference with obtaining breath samples that may beused in relation to different embodiments;

FIG. 14 is a flow diagram showing a method in accordance with variousembodiments for determining impairment based upon voice recordings;

FIG. 15 is a flow diagram showing a method in accordance with variousembodiments for determining impairment based upon movement information;

FIG. 16 is a flow diagram showing a method in accordance with variousembodiments for determining impairment based upon facial images; and

FIGS. 17 a-17 b are flow diagrams showing a method in accordance withsome embodiments for detecting drug based impairment; and

FIG. 18 is a flow diagram showing a method in accordance with someembodiments for applying a multi-predictor machine learning model thatis configured to yield an impairment classification based upon two ormore different types of data provided as respective predictors to themulti-predictor machine learning model.

DETAILED DESCRIPTION OF THE INVENTION

Various embodiments provide systems and methods for identifyingimpairment using measurement devices and trained models. Embodimentsdiscussed herein provide systems, methods, and/or devices that enableremote impairment testing that does not require a human monitor to bepresent or physically near the individual being monitored. Such anability is an improvement.

Some embodiments provide systems for determining proper use of a breathtester. Such systems include: a camera; a breath tube; one or moreprocessors configured to receive an image from the camera of a monitoredindividual blowing into the breath tube; and a non-transient computerreadable medium coupled to the one or more processors. The non-transientcomputer readable medium has stored therein instructions which whenexecuted by the one or more processors, causes the one or moreprocessors to: apply an interference classification model to the imageto yield a probability that the monitored individual is interfering withgas flowing from the monitored individual's mouth via the breath tube;indicate interference when the probability exceeds a first threshold;and indicate no interference when the probability is less than a secondthreshold.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to forward the image to a user for classification when theprobability is both less than the first threshold and greater than thesecond threshold. In various instances of the aforementionedembodiments, the non-transient computer readable medium further hasstored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to request that themonitored individual adjust the breath tube when the probability is bothless than the first threshold and greater than the second threshold.

In various instances of the aforementioned embodiments, thenon-transient computer readable medium further has stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to perform an impairment test of themonitored individual when the probability is less than the secondthreshold, wherein the impairment test is based upon a breath sample ofthe monitored individual received via the breath tube. In some suchinstances, the non-transient computer readable medium further has storedtherein instructions which when executed by the one or more processors,causes the one or more processors to report an impairment result of theimpairment test to a recipient device apart from the one or moreprocessors. In various such instances of the aforementioned embodiments,the impairment test includes at least one of: a breath based drugimpairment test, or a breath based alcohol impairment test.

In some instances of the aforementioned embodiments, the interferenceclassification model is a machine learning model trained using at leastone hundred images that have each been classified as exhibitinginterference or not exhibiting interference. In some such instances, theat least one hundred images depict at least ten different individualsundergoing a breath based impairment test.

Other embodiments provide methods for determining proper application ofa breath based impairment test. Such methods include: capturing an imageusing a camera of a monitored individual blowing into a breath tube;applying, by a hardware processing system, an interferenceclassification model to the image to yield a probability that themonitored individual is interfering with gas flowing from the monitoredindividual's mouth via the breath tube; comparing, by the hardwareprocessing system, the probability with a first threshold and generatingan indication of interference when the probability exceeds the firstthreshold; and comparing, by the processor, the probability with asecond threshold comparing, by the hardware processing system, theprobability with a first threshold and generating an indication of nointerference when the probability is less than the second threshold. Insome embodiments only a single image is used. In such embodiments, thesingle image may be extracted from, for example, a stream of imagesreceived from a camera. In other embodiments, multiple different imagesare used to evaluate. In such embodiments, the multiple images may beextracted from the same stream of images received from a camera.

Yet other embodiments provide non-transient computer readable media thathave stored therein instructions, which when executed by a hardwareprocessing system, cause the hardware processing system to: receive animage from a camera, wherein the image shows a monitored individualblowing into a breath tube; apply an interference classification modelto the image to yield a probability that the monitored individual isinterfering with gas flowing from the monitored individual's mouth viathe breath tube; compare the probability with a first threshold andgenerating an indication of interference when the probability exceedsthe first threshold; compare the probability with a second threshold andgenerating an indication of no interference when the probability is lessthan the second threshold; perform an impairment test of the monitoredindividual based at least in part on the indication of no interferenceand a breath sample of the monitored individual received via the breathtube; and report an impairment result of the impairment test to arecipient device apart from the one or more processors.

Some embodiments provide systems for detecting drug based impairment.Such systems include: a breath input device; a breath sensor configuredto receive a breath sample of an individual via the breath input deviceand to provide a sample value corresponding to the breath sample; one ormore processors; and a non-transient computer readable medium coupled tothe one or more processors. The non-transient computer readable mediumhas stored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to: receive the samplevalue; apply a drug impairment model to the sample data to yield aprobability that the individual is impaired; indicate a likelihood ofimpairment when the probability exceeds a first threshold; and indicateno impairment when the probability is less than a second threshold.

In some instances of the aforementioned embodiments where theprobability is a first probability, the system further include a camera.In such systems, the non-transient computer readable medium further hasstored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to: receive an imagecaptured by the camera of the individual blowing into the breath inputdevice; apply an interference classification model to the image to yielda second probability that the individual is interfering with gas flowingfrom the mouth of the individual via the breath input device; indicate alikelihood of interference when the second probability exceeds a thirdthreshold; and cause a request to be sent to the individual to modifyuse of the breath input device when the second probability exceeds athird threshold.

In various instances of the aforementioned embodiments where the systemfurther includes a camera, the non-transient computer readable mediumfurther has stored therein instructions which when executed by the oneor more processors, causes the one or more processors to: receive afacial image of the individual captured by the camera; and perform afacial image based impairment test using the facial image. In someinstances of the aforementioned embodiments where the sample value is afirst sample value and the breath sensor is further configured toprovide a second sample value corresponding to the breath sample, thenon-transient computer readable medium further has stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to perform a breath alcohol based impairmenttest using the second sample value.

In some instances of the aforementioned embodiments, the sample value isa level of a defined volatile organic compound. In some such instances,the defined volatile organic compound is one of: a volatile organiccompound indicative of methamphetamine, a volatile organic compoundindicative of marijuana, a volatile organic compound indicative ofcocaine, or a volatile organic compound indicative of heroin. In variousinstances of the aforementioned embodiments, the non-transient computerreadable medium further has stored therein instructions which whenexecuted by the one or more processors, causes the one or moreprocessors to forward the sample value to a user for classification whenthe probability is both less than the first threshold and greater thanthe second threshold. While the described embodiment discusses a samplevalue that is a level of a defined volatile organic compound, in otherembodiments multiple sample values may be generated from the same breathsample with each of the multiple sample values corresponding differentdefined volatile organic compounds. As such, the multiple sample valuesmay be indicative of a combination of defined organic compoundsincluding, but not limited to, a volatile organic compound indicative ofmethamphetamine and a volatile organic compound indicative of marijuana,a volatile organic compound indicative of methamphetamine and a volatileorganic compound indicative of cocaine, a volatile organic compoundindicative of methamphetamine and a volatile organic compound indicativeof heroin, a volatile organic compound indicative of marijuana and avolatile organic compound indicative of cocaine, a volatile organiccompound indicative of marijuana and a volatile organic compoundindicative of heroin, or a volatile organic compound indicative ofcocaine and a volatile organic compound indicative of heroin. Extendingthe example further, the multiple sample values may be indicative of acombination of three more defined organic compounds. Based upon thedisclosure provided herein, one of ordinary skill in the art willrecognize a variety of volatile organic compounds and/or combinationsthereof that may be processed in accordance with different embodiments.

In various instances of the aforementioned embodiments, thenon-transient computer readable medium further has stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to cause a request to be sent to theindividual to perform an additional impairment test. In some suchinstances, the additional impairment test is one of: a voice basedimpairment test, or a movement based impairment test. In some instancesof the aforementioned embodiments, the non-transient computer readablemedium further has stored therein instructions which when executed bythe one or more processors, causes the one or more processors to reportthe likelihood of impairment to a recipient device apart from the one ormore processors. In various instances of the aforementioned embodiments,the drug impairment model is a machine learning model trained usingbreath samples that have each been classified as exhibiting a definedvolatile organic compound corresponding a controlled substance.

In some instances of the aforementioned embodiments where the samplevalue is a first sample value and the breath sensor is furtherconfigured to provide a second sample value corresponding to the breathsample, the non-transient computer readable medium further has storedtherein instructions which when executed by the one or more processors,causes the one or more processors to perform a breath alcohol basedimpairment test using the second sample value. Ion various instances ofthe aforementioned embodiments, the non-transient computer readablemedium further has stored therein instructions which when executed bythe one or more processors, causes the one or more processors to performan additional impairment test when the probability is both less than thefirst threshold and greater than the second threshold. In suchinstances, the additional impairment test is one of: a voice basedimpairment test, a movement based impairment test, a facial image basedimpairment test, or a breath alcohol based impairment test.

Other embodiments provide methods for detecting use of a controlledsubstance. Such methods include: processing, by a breath sensor, abreath sample received from an individual; providing, by the breathsensor, a sample value corresponding to the breath sample; applying, bya processor, a drug impairment model to the sample data to yield aprobability that the individual is impaired; indicating, by theprocessor, a likelihood of usage when the probability exceeds a firstthreshold; and indicating, by the processor, no usage when theprobability is less than a second threshold.

Yet other embodiments provide non-transient computer readable media hasstored therein instructions, which when executed by a hardwareprocessing system cause the hardware processing system to: receive asample value, wherein the sample value is generated by a breath sensorbased upon a breath sample received from an individual via a breathinput device; apply a drug impairment model to the sample data to yielda probability that the individual is impaired; indicate a likelihood ofimpairment when the probability exceeds a first threshold; and indicateno impairment when the probability is less than a second threshold.

Some embodiments provide systems for detecting alcohol based impairment.Such systems include: a camera; a breath input device; a breath sensorconfigured to receive a breath sample of an individual via the breathinput device and to generate an alcohol level based upon the breathsample; one or more processors; and a non-transient computer readablemedium coupled to the one or more processors. The non-transient computerreadable medium has stored therein instructions which when executed bythe one or more processors, causes the one or more processors to:receive an image captured by the camera of the individual blowing intothe breath input device; apply an interference classification model tothe image to yield a probability that the individual is interfering withgas flowing from the mouth of the individual via the breath inputdevice; indicate a likelihood of no interference when the probability isless than a threshold; and based at least in part on the likelihood ofno interference, indicate the alcohol level as reliable.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to cause a request to be sent to the individual to perform anadditional impairment test. In some such instances, the additionalimpairment test is selected from a group consisting of: a voice basedimpairment test, and a movement based impairment test.

In various instances of the aforementioned embodiments where thethreshold is a first threshold, the non-transient computer readablemedium further has stored therein instructions which when executed bythe one or more processors, causes the one or more processors to:indicate a likelihood of interference when the probability exceeds asecond threshold; and cause a request to be sent to the individual tomodify use of the breath input device when the probability exceeds thesecond threshold. In some instances of the aforementioned embodiments,the non-transient computer readable medium further has stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: receive a facial image of the individualcaptured by the camera; and perform a facial image based impairment testusing the facial image. In some embodiments of the aforementionedembodiments, the non-transient computer readable medium further hasstored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to report the likelihoodof impairment to a recipient device apart from the one or moreprocessors.

In various instances of the aforementioned embodiments, the interferenceclassification model is a machine learning model trained using at leastone hundred images that have each been classified as exhibitinginterference or not exhibiting interference. In some such instances, theat least one hundred images depict at least ten different individualsundergoing a breath based impairment test.

In some instances of the aforementioned embodiments, the breath sensoris further configured to provide a sample value corresponding to thebreath sample, and the non-transient computer readable medium furtherhas stored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to perform a drug basedimpairment test using the sample value. In some such instances, thesample value is a level of a defined volatile organic compound.

Other embodiments provide methods for detecting use of a controlledsubstance. Such methods include: receiving, by a processor, an imagecaptured by a camera of an individual blowing into a breath inputdevice; processing, by a breath sensor, a breath sample derived from theindividual via a breath input device to yield an alcohol level in thebreath sample; applying, by the processor, an interferenceclassification model to the image to yield a probability that theindividual is interfering with gas flowing from the mouth of theindividual via the breath input device; indicating, by the processor, alikelihood of no interference when the probability is less than athreshold; and based at least in part on the likelihood of nointerference, indicating the alcohol level as reliable.

Yet other embodiments provide non-transient computer readable media thathave stored therein instructions, which when executed by a hardwareprocessing system cause the hardware processing system to: receive animage captured by a camera of an individual blowing into a breath inputdevice; receive an alcohol level from a breath sensor, wherein thealcohol level is generated by the breath sensor based upon a breathsample derived from the individual via a breath input device; apply aninterference classification model to the image to yield a probabilitythat the individual is interfering with gas flowing from the mouth ofthe individual via the breath input device; indicate the processor, alikelihood of no interference when the probability is less than athreshold; and based at least in part on the likelihood of nointerference, indicating the alcohol level as reliable.

Some embodiments provide systems for detecting impairment based uponvoice data. The system includes: a a microphone configured to receiveaudio information from an individual and to provide a voice datacorresponding to the audio information; one or more processors; and anon-transient computer readable medium coupled to the one or moreprocessors. The non-transient computer readable medium has storedtherein instructions which when executed by the one or more processors,causes the one or more processors to: receive the voice data from themicrophone; apply a voice impairment model to the voice data to yield aprobability that the individual is impaired; indicate a likelihood ofimpairment based at least in part on a determination that theprobability exceeds a first threshold; and indicate no impairment whenthe probability is less than a second threshold.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to apply an anomaly detection model to the voice data toyield an individual anomaly output. The likelihood of impairment isindicated when both the individual anomaly output indicates that thevoice data is an anomaly for the individual and the probability exceedsthe first threshold. In some cases, the anomaly detection model istrained using at least ten instances of voice data derived from theindividual.

In various instances of the aforementioned embodiments, the voiceimpairment model is a machine learning model trained using at least onehundred instances of voice data. In some such cases, the at least onehundred instances of voice data correspond to at least ten differentindividuals undergoing a voice based impairment test.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further having stored therein instructionswhich when executed by the one or more processors, causes the one ormore processors to cause a request to be sent to the individual toperform an additional impairment test. In some such cases the additionalimpairment test is one or more of: a facial image based impairment test,or a movement based impairment test.

In various instances of the aforementioned embodiments, thenon-transient computer readable medium further having stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to forward the voice data to a user forclassification when the probability is both less than the firstthreshold and greater than the second threshold. In some instances ofthe aforementioned embodiments, the non-transient computer readablemedium further having stored therein instructions which when executed bythe one or more processors, causes the one or more processors to reportthe likelihood of impairment to a recipient device apart from the one ormore processors.

Other embodiments provide methods for detecting impairment based uponvoice data. The methods include: receiving, by a processor, voice datacaptured by a microphone; applying, by the processor, a voice impairmentmodel to the voice data to yield a probability that the individual isimpaired; indicating, by the processor, a likelihood of impairment basedat least in part on a determination that the probability exceeds a firstthreshold; and indicating, by the processor, no impairment when theprobability is less than a second threshold.

Yet other embodiments provide non-transient computer readable mediahaving stored therein instructions, which when executed by a hardwareprocessing system cause the hardware processing system to: receive avoice data from the microphone, where the voice data corresponds to avoice of an individual; apply a voice impairment model to the voice datato yield a probability that the individual is impaired, where the voiceimpairment model is a machine learning model trained using at least onehundred instances of voice data and the at least one hundred instancesof voice data correspond to at least ten different individualsundergoing a voice based impairment test; indicate a likelihood ofimpairment based at least in part on a determination that theprobability exceeds a first threshold; and indicate no impairment whenthe probability is less than a second threshold.

Some embodiments provide systems for detecting impairment based uponmovement. Such systems include: a movement sensor configured to receivemovement information about a user detached monitor device; one or moreprocessors; and a non-transient computer readable medium coupled to theone or more processors. The non-transient computer readable medium hasstored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to: receive the movementinformation from the movement sensor; apply a movement impairment modelto the movement information to yield a probability that the individualis impaired; indicate a likelihood of impairment based at least in parton a determination that the probability exceeds a first threshold; andindicate no impairment when the probability is less than a secondthreshold.

In some instances of the aforementioned embodiments, the systems furtherinclude a camera. In some such instances, the non-transient computerreadable medium further has stored therein instructions which whenexecuted by the one or more processors, causes the one or moreprocessors to: receive an image of surroundings of the individual; andbased upon the image showing one or more physical supports around theindividual, cause a request for the individual to move to anotherlocation. In other such instances, the non-transient computer readablemedium further has stored therein instructions which when executed bythe one or more processors, causes the one or more processors to receivean image of surroundings of the individual. Indicating no impairment isbased at least in part on the image showing the individual located awayfrom a physical support. In various instances of the aforementionedembodiments, the movement impairment model is a machine learning modeltrained using at least one hundred instances of movement informationdata. In some such instances, the at least one hundred instances ofmovement information correspond to at least ten different individualsundergoing a movement based impairment test.

In various instances of the aforementioned embodiments, the systemsfurther include a camera and a display. In some such instances, thenon-transient computer readable medium further has stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: receive a face image of the individualindicating the individual is watching the display; and cause a videostream to play on the display. Indicating no impairment is based atleast in part on the face image of the individual indicating theindividual is watching the display.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to compare the movement information with a movementthreshold. Indicating no impairment is based at least in part on themovement information being greater than the movement threshold. Invarious instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to cause a request to be sent to the individual to perform anadditional impairment test. In some such instances, the additionalimpairment test is at least one of: a facial image based impairmenttest, and/or a voice based impairment test.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to forward the movement information to a user forclassification when the probability is both less than the firstthreshold and greater than the second threshold. In various instances ofthe aforementioned embodiments, the non-transient computer readablemedium further has stored therein instructions which when executed bythe one or more processors, causes the one or more processors to reportthe likelihood of impairment to a recipient device apart from the one ormore processors.

Other embodiments provide methods for detecting impairment based uponmovement information. Such methods include: receiving, by a processor,movement information from a movement sensor included in a user detachedmonitor device; applying, by the processor, a movement impairment modelto the movement information to yield a probability that the individualis impaired; indicating, by the processor, a likelihood of impairmentbased at least in part on a determination that the probability exceeds afirst threshold; and indicating, by the processor, no impairment whenthe probability is less than a second threshold.

Yet other embodiments provide non-transient computer readable mediahaving stored therein instructions, which when executed by a hardwareprocessing system cause the hardware processing system to: receivemovement information from a movement sensor; apply a movement impairmentmodel to the movement information to yield a probability that theindividual is impaired; indicate a likelihood of impairment based atleast in part on a determination that the probability exceeds a firstthreshold; and indicate no impairment when the probability is less thana second threshold. The movement impairment model is a machine learningmodel trained using at least one hundred instances of movementinformation data, and the at least one hundred instances of movementinformation correspond to at least ten different individuals undergoinga movement based impairment test.

Some embodiments provide systems for detecting impairment based uponfacial image. Such systems include: a camera configured to capture afacial image of an individual; one or more processors; and anon-transient computer readable medium coupled to the one or moreprocessors. The non-transient computer readable medium has storedtherein instructions which when executed by the one or more processors,causes the one or more processors to: receive the facial image of theindividual from the camera; apply a facial image impairment model to thefacial image to yield a probability that the individual is impaired;indicate a likelihood of impairment based at least in part on adetermination that the probability exceeds a first threshold; andindicate no impairment when the probability is less than a secondthreshold.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to: apply an anomaly detection model to the facial image toyield an individual anomaly output; and wherein the likelihood ofimpairment is indicated when both the individual anomaly outputindicates that the facial image is an anomaly for the individual and theprobability exceeds the first threshold. In some such instances, theanomaly detection model is trained using at least ten instances offacial images of the individual.

In various instances of the aforementioned embodiments, thenon-transient computer readable medium further has stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to cause a request to be sent to theindividual to perform an additional impairment test. In some suchinstances, the additional impairment test includes at least one of: avoice based impairment test, and a movement based impairment test.

In some instances of the aforementioned embodiments, the non-transientcomputer readable medium further has stored therein instructions whichwhen executed by the one or more processors, causes the one or moreprocessors to forward the facial image to a user for classification whenthe probability is both less than the first threshold and greater thanthe second threshold. In various instances of the aforementionedembodiments, the non-transient computer readable medium further hasstored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to report the likelihoodof impairment to a recipient device apart from the one or moreprocessors.

In various instances of the aforementioned embodiments, the facial imageimpairment model is a machine learning model trained using at least onehundred facial images. In some such instances, the at least one hundredfacial images correspond to at least ten different individualsundergoing a facial image based impairment test.

Other embodiments provide methods for detecting impairment based uponfacial images. Such methods include: receiving, by a processor, a facialimage of an individual from a camera; applying, by the processor, afacial image impairment model to the facial image to yield a probabilitythat the individual is impaired; indicating, by the processor, alikelihood of impairment based at least in part on a determination thatthe probability exceeds a first threshold;

and indicating, by the processor, no impairment when the probability isless than a second threshold. The facial image impairment model is amachine learning model trained using at least one hundred facial images,and the at least one hundred facial images correspond to at least tendifferent individuals undergoing a facial image based impairment test.

Yet other embodiments provide non-transient computer readable mediahaving stored therein instructions, which when executed by a hardwareprocessing system cause the hardware processing system to: receive afacial image of an individual from a camera; apply a facial imageimpairment model to the facial image to yield a probability that theindividual is impaired; indicate a likelihood of impairment based atleast in part on a determination that the probability exceeds a firstthreshold; and indicate no impairment when the probability is less thana second threshold. The facial image impairment model is a machinelearning model trained using at least one hundred facial images, and theat least one hundred facial images correspond to at least ten differentindividuals undergoing a facial image based impairment test

FIG. 1A shows a scenario where a monitored individual 105 is usingbreath based impairment detection device 192 including a breath tube 190that can be inserted into the mouth of monitored individual 105 while animage is taken of monitored individual 105 using a user detached monitordevice 120 having a camera with a field of view 110. Of note, while someembodiments are discussed herein as using a camera on one device to takean image and the impairment analysis tools from another device todiscern impairment, other embodiments may use a unified device where acamera is included in breath based impairment detection device 192. Inyet other embodiments, breath based impairment detection device 192and/or user detached monitor device 120 are capable of independentlydetermining impairment based upon one or more classes of data receivedabout monitored individual. In yet further embodiments, breath basedimpairment detection device 192 and/or user detached monitor device 120are capable of receiving one or more classes of data received aboutmonitored individual, and providing the received data to a centralmonitoring system (not shown) where the transferred information isprocessed to yield an indication that monitored individual is impairedor not. Based upon the disclosure provided herein, one of ordinary skillin the art will recognize a variety of combinations of hardware that maybe used to perform the impairment analysis in accordance with differentembodiments.

One or more impairment determinations and/or tamper determinations areperformed using a model trained to assess impairment and/or tamperingbased upon a respective one of the received classes of data aboutmonitored individual. In some instances, respective ones of the trainedmodels are trained using data specific to monitored individual 105 andin other instances other ones of the trained models are trained usinggeneric data from many individuals.

As an example, an interference model may be used that determines whetherbreath tube 190 is properly inserted into the mouth of monitoredindividual 105. This model may be trained using data from multipleindividuals. Once it is established that breath tube 190 is properlyinserted, a standard breathalyzer test may be performed to determine theblood alcohol level of monitored individual and thereby the alcoholbased impairment of monitored individual 105.

As another example, the previously discussed interference model may beused to determine whether breath tube 190 is properly inserted into themouth of monitored individual 105. Again, this model may be trainedusing data from multiple individuals. Once it is established that breathtube 190 is properly inserted, breath data received from monitoredindividual 105 is analyzed by a drug impairment model to determine alikelihood that monitored individual 105 is impaired. This drugimpairment model may be trained using data from multiple individuals.

As yet another example, voice data from monitored individual 105 may bereceived. A supervised anomaly model is applied to the received voicedata to determine if the voice data is within an expected range of voicedata from monitored individual 105. This supervised anomaly model istrained using data specific to monitored individual 105. Where ananomaly is determined, the voice data is processed by a voice impairmentmodel to determine a likelihood that monitored individual 105 isimpaired. This voice impairment model may be trained using data frommultiple individuals.

As an additional example, movement data from monitored individual 105may be received. A supervised anomaly model is applied to the receivedmovement data to determine if the movement data is within an expectedrange of movement data from monitored individual 105. This supervisedanomaly model is trained using data specific to monitored individual105. Where an anomaly is determined, the movement data is processed by amovement based impairment model to determine a likelihood that monitoredindividual 105 is impaired. This movement based impairment model may betrained using data from multiple individuals.

As yet a further example, facial image data from monitored individual105 may be received. A supervised anomaly model is applied to thereceived facial image data to determine if the facial image data iswithin an expected range of facial image data from monitored individual105. This supervised anomaly model is trained using data specific tomonitored individual 105. Where an anomaly is determined, the facialimage data is processed by a facial image based impairment model todetermine a likelihood that monitored individual 105 is impaired. Thisfacial image based impairment model may be trained using data frommultiple individuals.

FIG. 1B is a block diagram of a user detached monitor device 120including a forward camera 172 is shown that is usable in accordancewith one or more embodiments. User detached monitor device 120 includeswireless transceiver circuitry 128 that is capable of sending andreceiving information via wireless link (not shown) to/from wide areawireless network (not shown). Wireless transceiver circuitry 128 may beany circuitry, integrated circuit, and/or processor or controllercapable of supporting wireless communication. Such wirelesscommunication may include, but is not limited to, cellular telephonecommunication, Internet communication via a Wi-Fi access point, or both.In addition, user detached monitor device 120 includes a vibrator 102, aspeaker 104, and a visual display and touch screen 116. In some cases,at scheduled times a monitored individual associated with user detachedmonitor device 120 is alerted of a need to check-in. The schedule ofcheck-in times may be downloaded to a memory 124 by central monitoringstation 160 via wireless link 133. The monitored individual may bealerted by one or more of: a visual prompt via visual display and touchscreen 116, an audio prompt via speaker 114, and a tactile prompt viavibrator 112. Each of vibrator 112, speaker 114, and visual display andtouch screen 116 is communicatively coupled to memory 124 and/or acontroller circuit 122 for controlling the operations thereof. In somecases, controller circuit 122 includes a processor. In various cases,controller circuit 122 is part of an integrated circuit. In one or morecases, memory 124 is included in an integrated circuit with controllercircuit 122. In various cases, memory 124 may include non-transientinstructions (e.g., software or firmware-based based instructions)executable by controller circuit 122 to perform and/or enable variousfunctions associated with user detached monitor device 120. In someembodiments, controller circuit 122 executes instructions to perform oneor more of the impairment determination processes discussed below.

A visual prompt may include, but is not limited to, text, images and/ora combination thereof, or a series of such visual prompts. An audioprompt may include, but is not limited to, one or more different audioprompts, or a series thereof. Each prompt may be stored in memory 124and retrieved in accordance with the schedule that is also maintained inmemory 124. In some embodiments, alerting the monitored individualinvolves a prompt that includes an e-mail or text message generated by acentral monitoring station (e.g. a server supported website that is notshown) and transmitted to the e-mail account or cellular phone numbercorresponding to user detached monitor device 120. In particularembodiments, such a prompt may include a ‘post’ on the user's ‘wall,’‘feed,’ or other social networking privilege. In some embodiments, theprompt may comprise an automated or live phone call to the monitoredindividual.

User detached monitor device 120 further includes user identificationcircuitry 179 capable of gathering user identification information fromone or more of a microphone 171 (i.e., a voice data class), a forwardand/or reverse camera 172, 173 (i.e., an image data class), atemperature sensor 175 (i.e., an ambient temperature data class), and/ora biometric sensor 177 (i.e., a biometric data class). In some cases,user identification circuitry 179 is incorporated in an integratedcircuit with controller circuit 122. Microphone 171 is capable ofaccurately capturing the sound of a monitored individual's voice,forward and/or reverse cameras 172, 173 are each capable of accuratelycapturing images including, for example, an image of the monitoredindividual's face, temperature sensor 175 is capable of accuratelycapturing an ambient temperature around user detached monitor device120, and biometric sensor 177 is capable of accurately capturingbiometric data about the monitored individual including, but not limitedto, a thumb print, a retinal scan, or a breath-based alcoholmeasurement. Based upon the disclosure provided herein, one of ordinaryskill in the art will recognize a variety of biometric data andcorresponding sensors that may be used in relation to differentembodiments. Under the direction of control circuitry 122, useridentification circuitry 179 assembles one or more elements of datagathered by microphone 171, a camera 173, a temperature sensor 175,and/or a biometric sensor 177 into a user identification package whichis forwarded to central monitoring station 160 via wireless transceivercircuitry 128. User detached monitor device 120 additionally includes amotion detector 111 operable to discern whether user detached monitordevice is moving, and by implication whether a monitored individualholding user detached monitor device 120 is moving. In some cases,motion detector 120 includes an accelerometer circuit. Based upon thedisclosure provided herein, one of ordinary skill in the art willrecognize various circuits and/or sensors capable of indicating thatuser detached monitor device is moving that may be used in relation todifferent embodiments.

User detached monitor device 120 additionally includes locationdetection circuitry 126. Location detection circuitry 126 may includeone or more of, a GPS processing circuit capable of fixing a location ofuser detached monitor device 120 using GPS data, a WiFi based locationcircuit capable of fixing a location of user detached monitor device 120using contact information with one or more WiFi access points, and/or acell tower triangulation processing circuit capable of fixing a locationof user detached monitor device 120 using cell tower triangulation data.A local communication link 181 controls communication between userdetached monitor device 120 and breath based impairment detection device192. In some embodiments, local communication link 181 supports aBluetooth™ communication protocol and is capable of both receivinginformation from breath based impairment detection device 192 andtransmitting information to breath based impairment detection device192. In other embodiments, local communication link 181 supports a Wi-Ficommunication protocol and is capable of both receiving information frombreath based impairment detection device 192 and transmittinginformation to breath based impairment detection device 192. In somecases, local communication link 181 supports communication in only areceive or transmit direction. Based upon the disclosure providedherein, one of ordinary skill in the art will recognize a variety ofcommunication protocols and information transfer directions that may besupported by local communication link 181 in accordance with differentembodiments.

Additionally, user detached monitor device 120 includes a voice basedclassification engine 197, a movement based classification engine 198,and a visual based classification engine 199. Voice based classificationengine 197 is configured to apply voice data derived from microphone 171to both an anomaly determination model and a voice based impairmentdetection model to determine an impairment status of the monitoredindividual. In some cases, voice based classification engine 197performs processes similar to those discussed below in relation to FIG.14 .

Movement based classification engine 198 is configured to apply movementinformation derived from motion detector 111 to both an anomalydetermination model and a movement based impairment detection model todetermine an impairment status of the monitored individual. In somecases, movement based classification engine 197 performs processessimilar to those discussed below in relation to FIG. 15 .

Visual based classification engine 199 is configured to apply facialimage data derived from forward camera 172 to both an anomalydetermination model and a facial image based impairment detection modelto determine an impairment status of the monitored individual. In somecases, facial image based classification engine 199 performs processessimilar to those discussed below in relation to FIG. 16 . Additionally,visual based classification engine 199 is to apply visual image dataderived from forward camera 172 to an interference classification modelto determine if a monitored individual is attempting to tamper with abreath based test.

FIG. 1C is a block diagram of breath based impairment detection device192 capable of receiving input from monitored individual 105 via a tube190 at a breath sensor 166, and generating an impairment result by oneor both of an alcohol impairment classification engine 168 and a drugimpairment classification engine 169, with each under control of acontroller circuit 167. Breath sensor 166 may be, but is not limited to:a single test sensor capable of providing a single defined output value(e.g., alcohol value or a specific volatile organic compound (VOC)level), a multiple test sensor capable of providing multiple definedoutput values (e.g., alcohol value, a first specific volatile organiccompound (VOC) level, and a second specific VOC level), and/or acombination of two or more single test sensors each configured toprovide different defined output values. Instructions can be receivedvia a wide area transceiver 183 communicating via a wide area network(not shown) or via a WiFi transceiver 184 communicating via a WiFinetwork (not shown). Similarly, results from alcohol impairmentclassification engine 168 and/or drug impairment classification engine169 can be communicated via wide area transceiver 183 communicating orvia WiFi transceiver 184.

A local communication link 189 controls communication between breathbased impairment detection device 192 and user detached monitor device120. In some embodiments, local communication link 189 supports aBluetooth™ communication protocol and is capable of both receivinginformation from user detached monitor device 120 and transmittinginformation to user detached monitor device 120. In other embodiments,local communication link 189 supports a Wi-Fi communication protocol andis capable of both receiving information from user detached monitordevice 120 and transmitting information to user detached monitor device120. In some cases, local communication link 189 supports communicationin only a receive or a transmit direction. Based upon the disclosureprovided herein, one of ordinary skill in the art will recognize avariety of communication protocols and information transfer directionsthat may be supported by local communication link 189 in accordance withdifferent embodiments.

A physical breath interface 187 includes the structure to connect tobreath tube 190, and to transmit breath received from breath tube 190 toa breath sensor 166. Breath sensor may be any sensor or set of sensorsknown in the art that are capable of detecting volatile organiccompounds (VCOs) and/or alcohol within a breath sample. Based upon thedisclosure provided herein, one of ordinary skill in the art willrecognize a variety of sensors and/or combinations of sensors that maybe used in relation to different embodiments. Breath sensor 166 providescommunications indicating the level of VCOs and/or alcohol sensed in abreath received via physical breath interface.

The VCO information along with information from visual basedclassification engine 199 received via local communication link 189 andindicating any tampering with the breath based test are provided to drugimpairment classification engine 169. In some cases, drug impairmentclassification engine 169 performs various processes discussed below inrelation to FIGS. 17 a -17 b.

The alcohol information along with information from visual basedclassification engine 199 received via local communication link 189 andindicating any tampering with the breath based test are provided toalcohol impairment classification engine 168. In some cases, alcoholimpairment classification engine 168 performs various processesdiscussed below in relation to FIG. 16 .

Breath based impairment detection device 192 also includes a visualdisplay and touch screen 182. In some cases, at scheduled times amonitored individual associated with user Breath based impairmentdetection device 192 is alerted of a need to check-in. The schedule ofcheck-in times may be downloaded to a memory (not shown) included inbreath based impairment detection device 192 by a central monitoringstation (not shown). The monitored individual may be alerted by one ormore of: a visual prompt via visual display and touch screen 182. Insome cases, controller circuit 167 includes a processor. In variouscases, controller circuit 167 is part of an integrated circuit. In oneor more cases, the memory is included in an integrated circuit withcontroller circuit 167. In various cases, the memory may includenon-transient instructions (e.g., software or firmware-based basedinstructions) executable by controller circuit 167 to perform and/orenable various functions associated with breath based impairmentdetection device 192. In some embodiments, controller circuit 167executes instructions to perform one or more of the impairmentdetermination processes discussed below.

Turning to FIG. 1D, a block diagram is shown of a central monitoringsystem 2000 capable of communicating with one or both of user detachedmonitor device 120 and/or breath based impairment detection device 192,and performing multi-predictor impairment classification in accordancewith various embodiments. As shown, central monitoring system 2000includes a transceiver 2005 capable of receiving and sendingcommunications to/from various processing devices including, but notlimited to, user detached monitor device 120 and/or breath basedimpairment detection device 192.

The data transmitted via transceiver 2005 is provided from a controllercircuit 2010, and the data received via transceiver 2005 is provided tocontroller circuit 2010. In some cases, controller circuit 2010 includesa processor. In various cases, controller circuit 2010 is part of anintegrated circuit. In one or more cases, memory is included in anintegrated circuit with controller circuit 2010. In various cases, thememory may include non-transient instructions (e.g., software orfirmware-based based instructions) executable by controller circuit 2010to perform and/or enable various functions associated with centralmonitoring system. In some embodiments, controller circuit 2010 executesinstructions to perform one or more of the impairment determinationprocesses discussed below. Controller circuit 2010 is communicablycoupled to a memory 2020 where data may be stored and from which datamay be retrieved.

A data parsing module 2015 extracts data received via transceiver 2005to yield various classes of data (e.g., a voice data class, an imagedata class, an ambient temperature data class, a biometric data class, aVOC data class, a movement data class, a voice data class, and/or analcohol data class). Each of the different data classes may be stored indifferent locations in memory 2020 of central monitoring system 2000.

In some embodiments, central monitoring system 2000 receives dataindicating the likelihood that a monitored individual is impaired fromone or more different individual impairment processing enginesincluding, but not limited to, voice based classification engine 197,movement based classification engine 198, visual based classificationengine 199, alcohol impairment classification engine 168, and/or drugimpairment classification engine 169. A multi-predictor classificationengine 2050 applies a multi-predictor impairment model to a combinationof two or more likelihoods of impairment received from respectiveimpairment processing engines to yield a single likelihood of impairmentas a classification output 2075.

The multi-predictor impairment model is trained by a multi-predictorclassification training engine 2025. Multi-predictor classificationtraining engine 2025 uses sample data 2030 to train the multi-predictorimpairment model. Sample data 2030 includes two or more types of dataeach provided as respective predictors to multi-predictor classificationtraining engine 2025. Such sample data 2030 may include a combinationof, for example, two or more of movement data, facial image data, VOCsample data, or the like. Each of the aforementioned types of data mayinclude a number of previously received indications of likelihood ofimpairment that have been previously classified by an expert based upona user input 2002 with communication to the user providing the inputbeing provided via a display 2035, that that were automaticallyclassified by a classification engine from which the respective samplewas provided (e.g., one of voice based classification engine 197,movement based classification engine 198, visual based classificationengine 199, alcohol impairment classification engine 168, and/or drugimpairment classification engine 169). Multi-predictor classificationtraining engine 2025 may be any circuit and/or processor executinginstructions that is capable of training a multi-predictor impairmentmodel that receives two or more likelihood of impairment values, andadjusts the multi-predictor impairment model to improve the accuracy ofa classification output generated based upon applying themulti-predictor impairment model to two or more inputs.

FIG. 1E is a block diagram is shown of a central monitoring system 2100capable of communicating with one or both of user detached monitordevice 120 and/or breath based impairment detection device 192, andperforming multi-predictor impairment classification in accordance withvarious embodiments. As shown, central monitoring system 2100 includes atransceiver 2105 capable of receiving and sending communications to/fromvarious processing devices including, but not limited to, user detachedmonitor device 120 and/or breath based impairment detection device 192.

The data transmitted via transceiver 2105 is provided from a controllercircuit 2110, and the data received via transceiver 2105 is provided tocontroller circuit 2110. In some cases, controller circuit 2110 includesa processor. In various cases, controller circuit 2110 is part of anintegrated circuit. In one or more cases, memory is included in anintegrated circuit with controller circuit 2110. In various cases, thememory may include non-transient instructions (e.g., software orfirmware-based based instructions) executable by controller circuit 2110to perform and/or enable various functions associated with centralmonitoring system. In some embodiments, controller circuit 2110 executesinstructions to perform one or more of the impairment determinationprocesses discussed below. Controller circuit 2110 is communicablycoupled to a memory 2125 where data may be stored and from which datamay be retrieved.

A data parsing module 2120 extracts data received via transceiver 2105to yield various classes of data (e.g., a voice data class, an imagedata class, an ambient temperature data class, a biometric data class, aVOC data class, a movement data class, a voice data class, and/or analcohol data class). Each of the different data classes may be stored indifferent locations in memory 2125 of central monitoring system 2100.

In some embodiments, central monitoring system 2100 receives raw sensordata that may be used to determine a likelihood that a monitoredindividual is impaired. Such raw data may include, but is not limitedto, voice data from a monitored individual that may be processed by avoice based classification engine 2130, movement data for a monitoredindividual that may be processed by a movement based classificationengine 2135, facial image data for a monitored individual that may beprocessed by a facial image based classification engine 2140, breathalcohol data for a monitored individual that may be processed by abreath alcohol based classification engine 2145, and/or breath VOC datafor a monitored individual that may be processed by a breath drug basedclassification engine 2150.

In some cases, voice based classification engine 2130 performs someprocesses similar to those discussed below in relation to FIG. 14 ;movement based classification engine 2135 performs some processessimilar to those discussed below in relation to FIG. 15 ; facial imagebased classification engine 2140 performs some processes similar tothose discussed below in relation to FIG. 16 ; breath alcohol basedclassification engine 2145 performs some processes similar to thosediscussed below in relation to FIG. 12 ; and breath drug basedclassification engine 2150 performs some processes similar to thosediscussed below in relation to FIGS. 17 a -17 b.

A resulting likelihood of impairment based upon voice data 2131, aresulting likelihood of impairment based upon movement data 2136, aresulting likelihood of impairment based upon facial image data 2141, aresulting likelihood of impairment based upon breath alcohol data 2146,and a resulting likelihood of impairment based upon breath drug data2151 are provided to a classification output module and display 2155 andto a multi-predictor classification engine 2160. Classification outputmodule and display 2155 is configured to display the various reportedlikelihoods.

Multi-predictor classification engine 2160 applies a multi-predictorimpairment model to a combination of two or more likelihoods ofimpairment received from respective impairment processing engines toyield a single likelihood of impairment as a classification output 2175that is also provided to classification output module and display 2155.As discussed above in relation to multi-predictor classification engine2050 is trained by a multi-predictor classification training engine thatuses sample data to train the multi-predictor impairment model.

FIG. 2 is a flow diagram showing a method in accordance with someembodiments for configuring either user detached monitor device 120 orbreath based impairment detection device 192 to perform impairmentdetection using one or more trained models. Following flow diagram 200,it is determined if a configuration update has been received (block205). A configuration update may be received, for example, from acentral monitoring system. Such an update may be aa firmware update thatchanges the operational capability of the device receiving theconfiguration update. As just one example, a user may request that abreath based impairment detection device be changed to detect drug usagein additional to alcohol usage. In such a situation, an update to thefirmware may be made that will result in detection of VOCs in the breathof a monitored individual in addition to detecting alcohol in themonitored individual's breath. Such configuration updates may includeupdate machine learning models that are used in relation to respectiveimpairment detection processes. Based upon the disclosure providedherein, one of ordinary skill in the art will recognize a variety ofconfiguration updates that may be provided to one or both of userdetached monitor device 120 or breath based impairment detection device192 in accordance with different embodiments.

Where a configuration update is received (block 205), it is determinedwhether the received configuration update includes an update to a tamperconfiguration (block 210). Such a tamper configuration may be designedto assure that any impairment testing applied to a monitored individualis accurate. As just one example, a tamper configuration may beconfigured to determine whether a monitored individual is breathingproperly into breath tube 190 of breath based impairment detectiondevice 192. This process may be done, for example, similar to thatdiscussed below in relation to FIG. 12 . As another example, a tamperconfiguration may be configured to determine whether a monitoredindividual is standing too still during a movement based impairmentdetection process as more fully discussed below in relation to FIG. 15 .Based upon the disclosure provided herein, one of ordinary skill in theart will recognize a variety of tamper configurations that may bereceived in relation to different embodiments.

Where a tamper configuration is received (block 210), any tamperdetection modules associated with the receiving device are updated(block 215). Where, for example, the receiving device is breath basedbreath based impairment detection device 192 and the tamper isconfiguration is that of proper use of breath tube 190, the updatedtamper configuration may include an updated machine learning model(i.e., an interference classification model) that has been trained witha group of previously classified images of both tamper evident uses ofbreath tube 190 and proper uses of breath tube 190. Based upon thedisclosure provided herein, one of ordinary skill in the art willrecognize a number of tamper configurations and corresponding tamperdetection modules that may be updated in relation to differentembodiments.

Where either no tamper configuration was received (block 210) or areceived tamper configuration has been updated (block 215), it isdetermined if an impairment configuration has been received (block 220).Where, for example, the receiving device is breath based breath basedimpairment detection device 192 and the received impairmentconfiguration is an update to a drug impairment test, the updatedimpairment configuration may include an updated machine learning model(i.e., a drug impairment model) that has been trained with a group ofpreviously classified sets of breath data for both impaired andnon-impaired individuals as more fully described below in relation toFIGS. 17 a-17 b . As another example where the receiving device is userdetached monitor device 120 and the received impairment configuration isan update to a facial based impairment detection, the updated impairmentconfiguration may include an updated machine learning model (i.e., afacial impairment model) that has been trained with a group ofpreviously classified images of both impaired and non-impairedindividuals as more fully described below in relation to FIG. 16 . Basedupon the disclosure provided herein, one of ordinary skill in the artwill recognize a variety of impairment configurations that may bereceived in relation to different embodiments.

Where an impairment configuration is received (block 220), anyimpairment detection modules associated with the receiving device areupdated (block 225). Where, for example, the receiving device is breathbased breath based impairment detection device 192 and the receivedimpairment configuration is an update to a drug impairment test, theupdated impairment detection modules include breath sensor 166 and drugimpairment classification engine 169. As another example where thereceiving device is user detached monitor device 120 and the receivedimpairment configuration is an update to a facial based impairmentdetection, the updated impairment detection module may include visualbased classification engine 199. Based upon the disclosure providedherein, one of ordinary skill in the art will recognize a variety ofimpairment configurations that may be received in relation to differentembodiments.

Turning to FIG. 3 , a flow diagram 300 shows a method for training aninterference classification model based at least in part upon newlyreceived interference images in accordance with some embodiments.Following flow diagram 300, it is determined whether a userclassification of an image has been received (block 305). Imagesclassified by a user as either indicating interference with breath tube190 or no interference with breath tube 190 are valuable in training andre-training an interference classification model. User classificationinformation may be received, for example, as user input 2002 of centralmonitoring system 2000 or user input 2102 of central monitoring system2100. Based upon the disclosure provided herein, one of ordinary skillin the art will recognize a variety of mechanisms and processes that maybe used to associated user classification input data with correspondingimages.

A number of previously classified images showing a monitored individualwhile they are breathing into breath tube 190 may be included as sampledata 2030 and used to train the interference classification model. As astarter, the images included may include those where interference isobvious, those where no interference is obvious, and those whereinterference or non-interference is less obvious. By using a broad arrayof sample images, an increase in the accuracy of the interferenceclassification model can be achieved. As discussed below in relation toFIG. 12 user classification information may be requested in boundaryconditions where the result of applying the interference classificationmodel to an input image is ambiguous on whether the image showsinterference or not. Such images showing boundary conditions that areclassified by a user are valuable in increasing the accuracy of theinterference classification model.

Where a user classification of an image has been received (block 305),the classification and corresponding image are added to a database ofclassified interference images (block 310). In some embodiments, thisdatabase is incorporated into memory 2125 or sample data 2030 that maybe used in re-training the interference classification model that isused in relation to facial image based classification engine 2140 and/orvisual based classification engine 199. The classification andcorresponding image will indicate whether the image shows a personinterfering with breath tube 190 or not interfering with breath tube190. In some embodiments, classifications automatically indicated by theinterference classification model are included along withclassifications provided by a user as more fully discussed below inrelation to FIG. 12 . In other embodiments, only classificationsprovided by the user are updated to the database.

It is determined whether it is time to re-train the interferenceclassification model (block 315). This re-training may be periodicallyperformed based upon a passage of time or an increase in new samples.Where the re-training is done based upon the passage of time,determining that it is time to re-train the interference classificationmodel (block 315) is based upon a timer. Where, on the other hand, there-training is done based upon the availability of new samples,determining that it is time to re-train the interference classificationmodel (block 315) is based upon a count of newly available samples sincethe last training. Where it is determined that it is time to re-trainthe interference classification model (block 315), the database ofclassified interference images is accessed and used to train theinterference classification model (block 320). This re-training may bedone using any model training process known in the art.

Turning to FIG. 4 , a flow diagram 400 shows a method in accordance withsome embodiments for training a drug impairment model based at least inpart upon newly received drug impairment data. Following flow diagram400, it is determined whether a user classification of drug impairmentdata has been received (block 405). Drug impairment data classified by auser as either indicating impairment or non-impairment are valuable intraining and re-training a drug impairment model. User classificationinformation may be received, for example, as user input 2002 of centralmonitoring system 2000 or user input 2102 of central monitoring system2100. Based upon the disclosure provided herein, one of ordinary skillin the art will recognize a variety of mechanisms and processes that maybe used to associated user classification input data with correspondingdrug impairment data.

A number of previously classified drug impairment data sets derived fromthe breath of a monitored individual while they are breathing intobreath tube 190 may be included as sample data 2030 and used to trainthe drug impairment model. As a starter, the drug impairment data setsincluded may include those where impairment is obvious, those where noimpairment is obvious, and those where impairment or non-impairment isless obvious. By using a broad array of sample drug impairment datasets, an increase in the accuracy of the drug impairment model can beachieved. As discussed below in relation to FIGS. 17 a-17 b userclassification information may be requested in boundary conditions wherethe result of applying the drug impairment model to input drugimpairment data is ambiguous on whether the data shows impairment ornot. Such drug impairment data showing boundary conditions that areclassified by a user are valuable in increasing the accuracy of the drugimpairment model.

Where a user classification of drug impairment data has been received(block 405), the classification and corresponding drug impairment dataare added to a database of classified drug impairment data sets (block410). In some embodiments, this database is incorporated into memory2125 or sample data 2030 that may be used in re-training the drugimpairment model that is used in relation to breath drug classificationengine 2145 and/or drug impairment classification engine 169. Theclassification and corresponding drug impairment data will indicatewhether the data indicates drug impairment or not. In some embodiments,classifications automatically indicated by the drug impairment model areincluded along with classifications provided by a user as more fullydiscussed below in relation to FIGS. 17 a-17 b . In other embodiments,only classifications provided by the user are updated to the database.

It is determined whether it is time to re-train the drug impairmentmodel (block 415). This re-training may be periodically performed basedupon a passage of time or an increase in new samples. Where there-training is done based upon the passage of time, determining that itis time to re-train the drug impairment model (block 415) is based upona timer. Where, on the other hand, the re-training is done based uponthe availability of new samples, determining that it is time to re-trainthe drug impairment model (block 415) is based upon a count of newlyavailable samples since the last training. Where it is determined thatit is time to re-train the drug impairment model (block 415), thedatabase of classified drug impairment data sets is accessed and used totrain the drug impairment model (block 420). This re-training may bedone using any model training process known in the art.

Turning to FIG. 5 , a flow diagram 500 shows a method in accordance withsome embodiments for training a facial image based impairment modelbased at least in part upon newly received facial image data. Followingflow diagram 500, it is determined whether a user classification offacial image has been received (block 505). Facial images classified bya user as either indicating impairment or non-impairment are valuable intraining and re-training a facial image based impairment model. Userclassification information may be received, for example, as user input2002 of central monitoring system 2000 or user input 2102 of centralmonitoring system 2100. Based upon the disclosure provided herein, oneof ordinary skill in the art will recognize a variety of mechanisms andprocesses that may be used to associated user classification input datawith corresponding drug impairment data.

A number of previously classified facial images captured of a monitoredindividual may be included as sample data 2030 and used to train thefacial image based impairment model. As a starter, the facial images mayinclude those where impairment is obvious, those where no impairment isobvious, and those where impairment or non-impairment is less obvious.By using a broad array of facial imaged, an increase in the accuracy ofthe facial image based impairment model can be achieved. As discussedbelow in relation to FIG. 16 user classification information may berequested in boundary conditions where the result of applying the facialimage based impairment model to received facial images is ambiguous onwhether the data shows impairment or not. Such facial images showingboundary conditions that are classified by a user are valuable inincreasing the accuracy of the facial image based impairment model.

Where a user classification of a facial image has been received (block505), the classification and corresponding facial image are added to adatabase of classified facial impairment images (block 510). In someembodiments, this database is incorporated into memory 2125 or sampledata 2030 that may be used in re-training the facial image basedimpairment model that is used in relation to facial image classificationengine 2140 and/or visual based classification engine 199. Theclassification and corresponding facial image will indicate whether thedata indicates impairment or not. In some embodiments, classificationsautomatically indicated by the facial image based impairment model areincluded along with classifications provided by a user as more fullydiscussed below in relation to FIG. 16 . In other embodiments, onlyclassifications provided by the user are updated to the database.

It is determined whether it is time to re-train the facial image basedimpairment model (block 515). This re-training may be periodicallyperformed based upon a passage of time or an increase in new samples.Where the re-training is done based upon the passage of time,determining that it is time to re-train the facial image basedimpairment model (block 515) is based upon a timer. Where, on the otherhand, the re-training is done based upon the availability of newsamples, determining that it is time to re-train the facial image basedimpairment model (block 515) is based upon a count of newly availablesamples since the last training. Where it is determined that it is timeto re-train the facial image based impairment model (block 515), thedatabase of classified facial images is accessed and used to train thefacial image based impairment model (block 520). This re-training may bedone using any model training process known in the art.

Turning to FIG. 6 , a flow diagram 600 shows a method in accordance withvarious embodiments for maintaining a standard user facial imagedatabase updated with newly received facial images classified asnon-impaired. Following flow diagram 600, it is determined whether a newfacial image has been received (block 605). Where a new facial image hasbeen received (block 605), it is determined whether the facial image hasbeen classified as impaired (block 610). Where the newly received facialimage has been classified as non-impaired (block 610), the newlyreceived facial image is added to a database of facial images exclusiveto the particular monitored individual from whom the newly receivedfacial image was captured (block 615). Such images of the monitoredindividual in an unimpaired state are referred to as standard userfacial images and are used to make a threshold impairment decision asmore fully described below in relation to FIG. 16 . This database ofstandard user facial images may be deployed in any or a combination ofmemory 2125, sample data 2030, and/or memory 124. This database ofstandard user facial images is minimized to reduce the amount of memoryrequired to hold all of the collected facial images (block 620). Suchminimization may include removing the oldest facial images from thedatabase to assure that the database has the most recent images of themonitored individual, and/or to remove facial images that were onlymarginally classified as non-impaired (i.e., facial images that garneredrelatively high scores from the facial image based impairment modelcompared to other facial images in the database, but were nonethelessclassified as non-impaired). Based upon the disclosure provided herein,one of ordinary skill in the art will recognize a variety of minimizingprocesses that may be used in relation to different embodiments.

Turning to FIG. 7 , a flow diagram 700 shows a method in accordance withvarious embodiments for training a movement based impairment model basedat least in part upon newly received movement data. Following flowdiagram 700, it is determined whether a user classification of movementdata has been received (block 705). Movement data classified by a useras either indicating impairment or non-impairment are valuable intraining and re-training a movement based impairment model. Userclassification information may be received, for example, as user input2002 of central monitoring system 2000 or user input 2102 of centralmonitoring system 2100. Based upon the disclosure provided herein, oneof ordinary skill in the art will recognize a variety of mechanisms andprocesses that may be used to associated user classification input datawith corresponding movement data.

A number of previously movement data sets captured about a monitoredindividual may be included as sample data 2030 and used to train themovement based impairment model. As a starter, the movement data mayinclude that where impairment is obvious, that where no impairment isobvious, and that where impairment or non-impairment is less obvious. Byusing a broad array of movement data, an increase in the accuracy of themovement based impairment model can be achieved. As discussed below inrelation to FIG. 15 user classification information may be requested inboundary conditions where the result of applying the movement basedimpairment model to received facial images is ambiguous on whether thedata shows impairment or not. Such movement data showing boundaryconditions that are classified by a user are valuable in increasing theaccuracy of the movement based impairment model.

Where a user classification of movement data has been received (block705), the classification and corresponding movement data are added to adatabase of classified movement data sets (block 710). In someembodiments, this database is incorporated into memory 2125 or sampledata 2030 that may be used in re-training the facial image basedimpairment model that is used in relation to movement basedclassification engine 2135 and/or movement based classification engine198. The classification and corresponding movement data will indicatewhether the data indicates impairment or not. In some embodiments,classifications automatically indicated by the movement based impairmentmodel are included along with classifications provided by a user as morefully discussed below in relation to FIG. 15 . In other embodiments,only classifications provided by the user are updated to the database.

It is determined whether it is time to re-train the movement basedimpairment model (block 715). This re-training may be periodicallyperformed based upon a passage of time or an increase in new samples.Where the re-training is done based upon the passage of time,determining that it is time to re-train the movement based impairmentmodel (block 715) is based upon a timer. Where, on the other hand, there-training is done based upon the availability of new samples,determining that it is time to re-train the movement based impairmentmodel (block 715) is based upon a count of newly available samples sincethe last training. Where it is determined that it is time to re-trainthe movement based impairment model (block 715), the database ofclassified movement data sets is accessed and used to train the movementbased impairment model (block 720). This re-training may be done usingany model training process known in the art.

Turning to FIG. 8 , a flow diagram 800 shows a method in accordance withsome embodiments for maintaining a standard user movement databaseupdated with newly received movement data classified as non-impaired.Following flow diagram 800, it is determined whether a new movement datahas been received (block 805). Where a new movement data has beenreceived (block 805), it is determined whether the movement data hasbeen classified as impaired (block 810). Where the newly receivedmovement data been classified as non-impaired (block 810), the newlyreceived movement data is added to a database of movement data setsexclusive to the particular monitored individual about whom the newlyreceived movement data was captured (block 815). Such movement data ofthe monitored individual in an unimpaired state are referred to asstandard movement data and are used to make a threshold impairmentdecision as more fully described below in relation to FIG. 15 . Thisdatabase of standard user movement data sets may be deployed in any or acombination of memory 2125, sample data 2030, and/or memory 124. Thisdatabase of standard movement data sets is minimized to reduce theamount of memory required to hold all of the collected movement datasets (block 820). Such minimization may include removing the oldestmovement data sets from the database to assure that the database has themost recent movement data for the monitored individual, and/or to removemovement data sets that were only marginally classified as non-impaired(i.e., movement data sets that garnered relatively high scores from themovement based impairment model compared to other movement data sets inthe database, but were nonetheless classified as non-impaired). Basedupon the disclosure provided herein, one of ordinary skill in the artwill recognize a variety of minimizing processes that may be used inrelation to different embodiments.

Turning to FIG. 9 , a flow diagram 900 shows a method in accordance withvarious embodiments for training a voice data based impairment modelbased at least in part upon newly received voice data. Following flowdiagram 900, it is determined whether a user classification of voicedata has been received (block 905). Voice data classified by a user aseither indicating impairment or non-impairment are valuable in trainingand re-training a voice based impairment model. User classificationinformation may be received, for example, as user input 2002 of centralmonitoring system 2000 or user input 2102 of central monitoring system2100. Based upon the disclosure provided herein, one of ordinary skillin the art will recognize a variety of mechanisms and processes that maybe used to associated user classification input data with correspondingvoice data.

A number of previously voice data sets captured about a monitoredindividual may be included as sample data 2030 and used to train thevoice based impairment model. As a starter, the voice data may includethat where impairment is obvious, that where no impairment is obvious,and that where impairment or non-impairment is less obvious. By using abroad array of voice data, an increase in the accuracy of the voicebased impairment model can be achieved. As discussed below in relationto FIG. 14 user classification information may be requested in boundaryconditions where the result of applying the voice based impairment modelto received facial images is ambiguous on whether the data showsimpairment or not. Such voice data showing boundary conditions that areclassified by a user are valuable in increasing the accuracy of thevoice based impairment model.

Where a user classification of voice data has been received (block 905),the classification and corresponding voice data are added to a databaseof classified voice data sets (block 910). In some embodiments, thisdatabase is incorporated into memory 2125 or sample data 2030 that maybe used in re-training the facial image based impairment model that isused in relation to voice based classification engine 2130 and/or voicebased classification engine 197. The classification and correspondingvoice data will indicate whether the data indicates impairment or not.In some embodiments, classifications automatically indicated by thevoice based impairment model are included along with classificationsprovided by a user as more fully discussed below in relation to FIG. 15. In other embodiments, only classifications provided by the user areupdated to the database.

It is determined whether it is time to re-train the voice basedimpairment model (block 915). This re-training may be periodicallyperformed based upon a passage of time or an increase in new samples.Where the re-training is done based upon the passage of time,determining that it is time to re-train the voice based impairment model(block 915) is based upon a timer. Where, on the other hand, there-training is done based upon the availability of new samples,determining that it is time to re-train the voice based impairment model(block 915) is based upon a count of newly available samples since thelast training. Where it is determined that it is time to re-train thevoice based impairment model (block 915), the database of classifiedvoice data sets is accessed and used to train the voice based impairmentmodel (block 920). This re-training may be done using any model trainingprocess known in the art.

Turning to FIG. 10 , a flow diagram 1000 shows a method in accordancewith some embodiments for maintaining a standard user voice databaseupdated with newly received voice data classified as non-impaired.Following flow diagram 1000, it is determined whether a new voice datahas been received (block 1005). Where a new voice data has been received(block 1005), it is determined whether the voice data has beenclassified as impaired (block 1010). Where the newly received voice databeen classified as non-impaired (block 1010), the newly received voicedata is added to a database of voice data sets exclusive to theparticular monitored individual about whom the newly received voice datawas captured (block 1015). Such voice data of the monitored individualin an unimpaired state are referred to as standard voice data and areused to make a threshold impairment decision as more fully describedbelow in relation to FIG. 14 . This database of standard user voice datasets may be deployed in any or a combination of memory 2125, sample data2030, and/or memory 124. This database of standard voice data sets isminimized to reduce the amount of memory required to hold all of thecollected voice data sets (block 1020). Such minimization may includeremoving the oldest voice data sets from the database to assure that thedatabase has the most recent voice data for the monitored individual,and/or to remove voice data sets that were only marginally classified asnon-impaired (i.e., voice data sets that garnered relatively high scoresfrom the voice based impairment model compared to other voice data setsin the database, but were nonetheless classified as non-impaired). Basedupon the disclosure provided herein, one of ordinary skill in the artwill recognize a variety of minimizing processes that may be used inrelation to different embodiments.

Turning to FIG. 11 , a flow diagram 1100 shows a method in accordancewith some embodiments for determining impairment using selectivelyapplied impairment models and processes. Following flow diagram 1100, itis determined whether a request for an impairment test has been received(block 1105). Such a request may be received, for example, from asupervising official charged with determining an impairment status of amonitored individual. As another example, such a request may bepre-programmed to occur in accordance with a schedule. Based upon thedisclosure provided herein, one of ordinary skill in the art willrecognize a variety of mechanisms and/or processes that may be used inreceiving an impairment test request, and/or a number of individualsand/or pre-programmed schedules that may be responsible for making suchrequests. The received request for an impairment test may request one ormore impairment tests.

Of note, not all systems will provide an ability to perform all tests.For example, an impairment testing system including only breath basedimpairment device 192 standing alone. In such a system, it could be thatonly drug based impairment testing and/or alcohol based impairmenttesting are offered. As another example, an impairment testing systemincluding only user detached monitor device 120 standing alone. In sucha system, it could be that only voice based impairment testing, movementbased impairment testing, and/or facial image based impairment testing.In a system including both breath based impairment device 192 and userdetached monitor device 120, any or all of voice based impairmenttesting, movement based impairment testing, facial image basedimpairment testing, drug based impairment testing, and/or alcohol basedimpairment testing are offered. As yet another example of a systemincluding a central processing system, breath based impairment device192, and user detached monitor device 120, any or all of voice basedimpairment testing, movement based impairment testing, facial imagebased impairment testing, drug based impairment testing, alcohol basedimpairment, and/or multi-predictor impairment testing are offered. Basedupon the disclosure provided herein, one of ordinary skill in the artwill recognize a variety of equipment combinations and/or impairmenttesting capabilities that can be offered in relation to differentembodiments.

Where a request for an impairment test is received (block 1105), it isdetermined if the received request includes a request to perform analcohol impairment test (block 1110). Where an alcohol impairment testis requested (block 1110), an alcohol impairment test is performed(block 1115). This is shown in a dashed line because the process isdescribed in more detail below in relation to FIG. 12 below.

Once either the alcohol impairment test has been performed (block 1115)or no alcohol impairment test was requested (block 1110), it isdetermined if the received request includes a request to perform a voiceimpairment test (block 1120). Where a voice impairment test is requested(block 1120), a voice impairment test is performed (block 1125). This isshown in a dashed line because the process is described in more detailbelow in relation to FIG. 14 below.

Once either the voice impairment test has been performed (block 1125) orno voice impairment test was requested (block 1120), it is determined ifthe received request includes a request to perform a movement impairmenttest (block 1130). Where a movement impairment test is requested (block1130), a movement impairment test is performed (block 1135). This isshown in a dashed line because the process is described in more detailbelow in relation to FIG. 15 below.

Once either the movement impairment test has been performed (block 1135)or no movement impairment test was requested (block 1130), it isdetermined if the received request includes a request to perform afacial impairment test (block 1140). Where a facial impairment test isrequested (block 1140), a facial impairment test is performed (block1145). This is shown in a dashed line because the process is describedin more detail below in relation to FIG. 16 below.

Once either the facial impairment test has been performed (block 1145)or no facial impairment test was requested (block 1140), it isdetermined if the received request includes a request to perform a drugimpairment test (block 1150). Where a drug impairment test is requested(block 1150), a drug impairment test is performed (block 1155). This isshown in a dashed line because the process is described in more detailbelow in relation to FIGS. 17 a-17 b below.

Once either the drug impairment test has been performed (block 1155) orno drug impairment test was requested (block 1150), it is determined ifthe received request includes a request to perform a multi-predictorimpairment test (block 1160). Where a multi-predictor impairment test isrequested (block 1160), a multi-predictor impairment test is performed(block 1165). This is shown in a dashed line because the process isdescribed in more detail below in relation to FIG. 18 below.

Turning to FIG. 12 , a flow diagram 1200 shows a method in accordancewith various embodiments for determining impairment based upon breathalcohol measurements. Flow diagram 1200 represents block 1115 and thusbegins from block 1110 of the previously described FIG. 11 . Theprocesses of flow diagram 1200 may be executed on one of user detachedmonitor device 120 or breath based impairment detection device 192depending upon the system implementation, and/or a combination of one ofuser detached monitor device 120 or breath based impairment detectiondevice 192 and one of central monitoring system 2000 or centralmonitoring system 2100. Following flow diagram 1200, a request is madefor the monitored individual to blow into breath tube 190 while taking avideo using one of forward camera 172 or reverse camera 173 of userdetached monitor device including the monitored individual's face andsurrounding region while blowing (block 1205). This request may beprovided via a display on either or both of user detached monitor device120 and/or breath based impairment detection device 192.

A sensor on breath based impairment detection device 192 detects whetherat least a minimum gas flow is reported from breath tube 190 (block1210). This sensing may be done using any sensor known in the art fordetecting gas flow volume. The sensor may be included as part of breathsensor 166 in breath based impairment detection device 192, with theinformation from the sensor being provided to one or both of alcoholimpairment classification engine 168 and/or drug impairmentclassification engine 169. Where it is determined that insufficient gasflow has been detected by the sensor (block 1210), it is determinedwhether a timeout condition has been met (block 1215). A monitoredindividual is given a defined time period to comply with the request,and after the defined time period has passed the timeout condition ismet. If the timeout condition has been met (block 1220), a timeout erroris indicated (block 1220) and the processing returns to block 1120 ofFIG. 11 without rendering an analysis of whether the monitoredindividual is impaired by alcohol.

Alternatively, where it is determined that sufficient gas flow has beendetected by the sensor (block 1210), a still image from the video of themonitored individual's face and surrounding area is isolated (block1225). An interference classification model is applied to the stillimage to yield an indication of whether the monitored individual isinterfering with breath tube (block 1230). As discussed above inrelation to FIG. 3 , the interference classification model is trainedusing a number of previously classified images showing a monitoredindividual while they are breathing into breath tube 190. The images maybe derived from a large number of different monitored individuals, andhave been classified as either indicating interference with breath tube190 or no interference with breath tube 190. In some cases theclassification is based upon a user input 2002 of central monitoringsystem 2000 or user input 2102 of central monitoring system 2100. Inother cases, the classification is based upon the output from the userclassification model. FIG. 13 a are drawings representing a number ofexample still images 1302, 1304, 1306, 1308, 1310 each showing anrespective individual with a breath tube properly inserted in theirmouth while breathing, and without any addition elements included in theimage that appear to be interfering with the test. FIG. 13 b aredrawings representing a number of example still images 1352, 1354, 1356,1358 each showing an respective individual interfering with a breathtube. Still image 1352 shows an individual with both the breath tube anda secondary tube inserted into their mouth. In such an approach, a gascan be blown into the individual's mouth that flows out through thebreath tube simulating breath. Still image 1354 shows an individual withthe breath tube inserted in their mouth, and a secondary tube connectedto into the breath tube. In such an approach, a gas can be blown fromthe secondary tube into the breath tube simulating breath. Still image1356 shows an individual with the breath tube inserted in their mouth,but their hand is over their mouth and their head is turned potentiallyhiding a secondary tube. Still image 1356 shows an individual with thebreath tube near their mouth along with a secondary tube. In such anapproach, a gas can be blown toward the individual's mouth from thesecondary tube and into the breath tube simulating breath.

In some embodiments, the interference classification model is aTensorFlow™ backbone used to generate a model that can be exported to aselected platform. Based upon the disclosure provided herein, one ofordinary skill in the art will recognize a variety of machine learningmodel types that may be trained using images of individuals blowing in abreath tube to determine whether a newly received image shows anindividual properly using the breath tube.

The output from the interference classification model indicates alikelihood that the monitored individual is interfering with breath tube190 or not using it properly. It is determined whether the likelihoodvalue received from the interference classification model is higher thana high probability value (block 1235). Such a high probability value isselected as high enough to warrant indicating interference withouthaving a human user consider the image. In some embodiments the highprobability value is user programmable. Where the likelihood valuereceived from the interference classification model is higher than thehigh probability value (block 1235), an interference is indicated (block1240) and the processing returns to block 1120 of FIG. 11 withoutrendering an analysis of whether the monitored individual is impaired byalcohol.

Where, on the other hand, the likelihood value received from theinterference classification model is not higher than the highprobability value (block 1235), it is determined whether the likelihoodvalue received from the interference classification model is lower thana low probability value (block 1250). Such a low probability value isselected as low enough to warrant indicating no interference withouthaving a human user consider the image. In some embodiments the lowprobability value is user programmable. Where the likelihood valuereceived from the interference classification model is lower than thelow probability value (block 1250), no interference is indicated (block1265). In this situation, alcohol impairment testing applied to thereceived breath sample is recorded as impairment results (block 1270),and the impairment results are reported (block 1275). In someembodiments, the alcohol impairment testing is a standard breath basedalcohol detection as is known in the art. Having reported the impairmentresults, the process then returns to block 1120 of FIG. 11 .

Alternatively, where the likelihood value received from the interferenceclassification model is not lower than the low probability value (block1250), an ambiguous condition has occurred. In such a situation, thestill image of the monitored individual is forwarded to a user forclassification (block 1255). The user may be, for example, a humanresponsible for making final determinations of interference based uponthe still image. In some cases, the human may be a supervising authorityof the monitored individual. This process may forward the image to acentral monitoring system (e.g., central monitoring system 2100) whichis capable of accepting user input (e.g., user input 2102). The userclassification (i.e., classified as interference or no interference) isstored to a database where it may be used in a future re-training of theinterference classification model as discussed above in relation to FIG.3 .

In addition, a request is made to the monitored individual to adjust howthey are using the breath tube (block 1260). This request may be made,for example, by an audio message played on user detached monitor device120. Based upon the disclosure provided herein, one of ordinary skill inthe art will recognize a variety of mechanisms and/or processes that maybe used to deliver a request to the monitored individual in accordancewith different embodiments. The process then returns to block 1205 wherethe process begins again.

Turning to FIG. 14 , a flow diagram 1400 shows a method in accordancewith various embodiments for determining impairment based upon voicerecordings. Flow diagram 1400 represents block 1125 and thus begins fromblock 1120 of the previously described FIG. 11 . The processes of flowdiagram 1400 may be executed on user detached monitor device 120 and/ora combination of user detached monitor device 120 and one of centralmonitoring system 2000 or central monitoring system 2100. Following flowdiagram 1400, a message is displayed to the monitored individual usingvisual display and touch screen 116 of user detached monitor device 120(block 1405). The message requests the monitored individual to read themessage and record audio of them reading the message. The audio can berecorded using microphone 171 of user detached monitor device 120.

It is determined whether the monitored individual has completed readingand recording the message (block 1410). Where the message has not beencompleted (block 1410), it is determined whether a timeout condition hasbeen met (block 1415). A monitored individual is given a defined timeperiod to comply with the request, and after the defined time period haspassed the timeout condition is met. If the timeout condition has beenmet (block 1415), a timeout error is indicated (block 1420) and theprocessing returns to block 1130 of FIG. 11 without rendering ananalysis of whether the monitored individual is impaired by alcohol.

Alternatively, where it is determined that the message has beencompleted (block 1410), an anomaly detection is performed on therecorded message (block 1430). Such anomaly detection may be performedby any machine learning process designed to detect anomalous sounds inthe user's voice. As such, the anomaly detection is performed by amachine learning model that has been trained with a number of instancesof the monitored individual's voice that were recorded when themonitored individual was not impaired. In some embodiments, such voicedata is collected as discussed above in relation to FIG. 10 . Based uponthe disclosure provided herein, one of ordinary skill in the art willrecognize a variety of types of machine learning models that may betrained to perform voice anomaly detection in relation to differentembodiments.

Where an anomaly is not detected in the monitored individual's voicedata (i.e., the monitored individual sounds the same as they alwayssound) (block 1435), no anomaly is indicated (block 1440) and noimpairment is indicated (block 1465). At this juncture, the processingreturns to block 1130 of FIG. 11 without rendering an analysis ofwhether the monitored individual is impaired.

Alternatively, where an anomaly is detected (i.e., the monitoredindividual sounds different from the way they always sound) (block1435), a voice impairment model is applied to the recorded message toyield an indication of whether the monitored individual is impaired by,for example, drugs or alcohol (block 1445). The voice impairment modelmay be implemented in, for example, voice based classification engine2130 or voice based classification engine 197 depending upon theparticular implementation. As discussed above in relation to FIG. 9 ,the voice impairment model is trained using a number of previouslyclassified voice based impairment data. The voice based impairment datamay be derived from a large number of different monitored individuals,and have been classified as either indicating impairment or not. In somecases the classification is based upon a user input 2002 of centralmonitoring system 2000 or user input 2102 of central monitoring system2100. In other cases, the classification is based upon the output fromthe voice impairment model. In some embodiments, the voice impairmentmodel is a TensorFlow™ backbone used to generate a model that can beexported to a selected platform. Based upon the disclosure providedherein, one of ordinary skill in the art will recognize a variety ofmachine learning model types that may be trained using recorded audiodata from tested individuals to determine whether a newly recordedmessage shows whether an individual is impaired or not.

The output from the voice impairment model indicates a likelihood thatthe monitored individual is impaired based upon patterns in the audioreceived from the monitored individual. It is determined whether thelikelihood value received from the voice impairment model is higher thana high probability value (block 1450). Such a high probability value isselected as high enough to warrant indicating impairment without havinga human user consider the recently received recorded message from themonitored individual. In some embodiments the high probability value isuser programmable. Where the likelihood value received from the voiceimpairment model is higher than the high probability value (block 1450),impairment of the monitored individual is indicated and reported (block1455) and the processing returns to block 1130 of FIG. 11 .

Where, on the other hand, the likelihood value received from the voiceimpairment model is not higher than the high probability value (block1450), it is determined whether the likelihood value received from thevoice impairment model is lower than a low probability value (block1460). Such a low probability value is selected as low enough to warrantindicating no impairment without having a human user consider therecently received recorded message. In some embodiments the lowprobability value is user programmable. Where the likelihood valuereceived from the voice impairment model is lower than the lowprobability value (block 1460), no impairment is indicated or reported(block 1465) and the processing returns to block 1130 of FIG. 11 .

Alternatively, where the likelihood value received from the voiceimpairment model is not lower than the low probability value (block1460), an ambiguous condition has occurred. In such a situation, therecently received recorded message is forwarded to a user capable ofclassifying the data as indicative of impairment or not (block 1470).The user may be, for example, a human responsible for making finaldeterminations of impairment based at least in part upon the recordedvoice message. In some cases, the human may be a supervising authorityof the monitored individual. This process may forward the recorded voicemessage to a central monitoring system (e.g., central monitoring system2100) which is capable of presenting the data to a user and storing therecorded voice message and user classification together in a database.Where the user indicates impairment (block 1475) the impairment isindicated and reported (block 1455), and the processing returns to block1130 of FIG. 11 . Alternatively, where the user indicates no impairment(block 1475), no impairment is indicated or reported (block 1465) andthe processing returns to block 1130 of FIG. 11 .

In some embodiments, where the likelihood value received from the drugimpairment model is not lower than the low probability value (block1460) indicating the aforementioned ambiguous condition has occurred, itis determined if an additional impairment test should be run (block1494). Where an additional impairment test is to be run (block 1494),one or more additional impairment tests are performed (block 1496). Theadditional impairment test(s) may include one or more of: an alcoholimpairment test similar to that discussed herein in relation to FIG. 12, a movement based impairment test similar to that discussed herein inrelation to FIG. 15 , a facial image based impairment test similar tothat discussed herein in relation to FIG. 16 , and/or a drug basedimpairment test similar to that discussed herein in relation to FIGS. 17.

Turning to FIG. 15 a flow diagram 1500 shows a method in accordance withvarious embodiments for determining impairment based upon movementinformation. Flow diagram 1400 represents block 1135 and thus beginsfrom block 1130 of the previously described FIG. 11 . The processes offlow diagram 1500 may be executed on user detached monitor device 120and/or a combination of user detached monitor device 120 and one ofcentral monitoring system 2000 or central monitoring system 2100.Following flow diagram 1500, a message is displayed to the monitoredindividual using visual display and touch screen 116 of user detachedmonitor device 120 (block 1505). The message requests the monitoredindividual move to the center of a room where there are no supports, andstand still while watching a disorienting video stream on visual displayand touch screen 116. The disorienting video stream may be, but is notlimited to, two concentric rings rotating in opposite directions. Basedupon the disclosure provided herein, one of ordinary skill in the artwill recognize a variety of disorienting video streams that may be usedin relation to different embodiments. While the user is watching thedisorienting video stream, the movement data of the monitored individualis recorded as recorded movement data. The movement data may be sensedby motion detector 111 of user detached monitor device 120 and recordedto memory 124 by controller circuit 122.

It is determined whether video received from forward camera 172 of userdetached monitor device 120 shows that the monitored individual islocated at the center of a room away from supports and that themonitored individual is looking at visual display and touch screen 116of user detached monitor device 120 (block 1510). Once the videoindicates that the monitored individual is complying (block 1510), it isdetermined whether sufficient the recorded movement data indicatessufficient movement (block 1515). When standing still there is almostalways some movement unless the monitored individual is improperlyrelying upon some type of support. Thus, the system looks for a definedthreshold of movement. This defined threshold may be user programmable,and in some embodiments the defined threshold is specific to themonitored individual.

Where insufficient movement is detected (block 1515), such is indicatedas an error (block 1520) and a timeout condition is tested (block 1525).A monitored individual is given a defined time period to comply with therequest, and after the defined time period has passed the timeoutcondition is met. If the timeout condition has been met (block 1525), atimeout error is indicated (block 1530) and the processing returns toblock 1140 of FIG. 11 without rendering an analysis of whether themonitored individual is impaired.

Alternatively, where sufficient movement is detected (block 1515), amovement impairment model is applied to the recorded movement data toyield an indication of whether the monitored individual is impaired by,for example, drugs or alcohol (block 1540). The movement impairmentmodel may be implemented in, for example, movement based classificationengine 2135 or movement based classification engine 198 depending uponthe particular implementation. As discussed above in relation to FIG. 7, the movement impairment model is trained using a number of previouslyclassified recorded movement data sets. The movement based impairmentdata may be derived from a large number of different monitoredindividuals, and have been classified as either indicating impairment ornot. In some cases the classification is based upon a user input 2002 ofcentral monitoring system 2000 or user input 2102 of central monitoringsystem 2100. In other cases, the classification is based upon the outputfrom the movement impairment model. In some embodiments, the movementimpairment model is a TensorFlow™ backbone used to generate a model thatcan be exported to a selected platform. Based upon the disclosureprovided herein, one of ordinary skill in the art will recognize avariety of machine learning model types that may be trained usingrecorded audio data from tested individuals to determine whether a newlyrecorded message shows whether an individual is impaired or not.

The output from the movement impairment model indicates a likelihoodthat the monitored individual is impaired based upon movement of themonitored individual when they are expected to be standing still. It isdetermined whether the likelihood value received from the movementimpairment model is higher than a high probability value (block 1555).Such a high probability value is selected as high enough to warrantindicating impairment without having a human user consider the recentlyreceived recorded movement data from the monitored individual. In someembodiments the high probability value is user programmable. Where thelikelihood value received from the movement impairment model is higherthan the high probability value (block 1555), impairment of themonitored individual is indicated and reported (block 1580) and theprocessing returns to block 1140 of FIG. 11 .

Where, on the other hand, the likelihood value received from themovement impairment model is not higher than the high probability value(block 1555), it is determined whether the likelihood value receivedfrom the movement impairment model is lower than a low probability value(block 1560). Such a low probability value is selected as low enough towarrant indicating no impairment without having a human user considerthe recently received movement data. In some embodiments the lowprobability value is user programmable. Where the likelihood valuereceived from the movement impairment model is lower than the lowprobability value (block 1560), no impairment is indicated or reported(block 1575) and the processing returns to block 1140 of FIG. 11 .

Alternatively, where the likelihood value received from the movementimpairment model is not lower than the low probability value (block1560), an ambiguous condition has occurred. In such a situation, therecently received recorded movement data is forwarded to a user capableof classifying the data as indicative of impairment or not (block 1565).The user may be, for example, a human responsible for making finaldeterminations of impairment based at least in part upon the movementdata. In some cases, the human may be a supervising authority of themonitored individual. This process may forward the recorded movementdata to a central monitoring system (e.g., central monitoring system2100) which is capable of presenting the data to a user and storing themovement data and user classification together in a database. Where theuser indicates impairment (block 1570) the impairment is indicated andreported (block 1580), and the processing returns to block 1140 of FIG.11 . Alternatively, where the user indicates no impairment (block 1570),no drug impairment is indicated or reported (block 1575) and theprocessing returns to block 1140 of FIG. 11 .

In some embodiments, where the likelihood value received from the drugimpairment model is not lower than the low probability value (block1560) indicating the aforementioned ambiguous condition has occurred, itis determined if an additional impairment test should be run (block1594). Where an additional impairment test is to be run (block 1594),one or more additional impairment tests are performed (block 1596). Theadditional impairment test(s) may include one or more of: an alcoholimpairment test similar to that discussed herein in relation to FIG. 12, a voice based impairment test similar to that discussed herein inrelation to FIG. 14 , a facial image based impairment test similar tothat discussed herein in relation to FIG. 16 , and/or a drug basedimpairment test similar to that discussed herein in relation to FIGS. 17.

Turning to FIG. 16 , a flow diagram 1600 shows a method in accordancewith various embodiments for determining impairment based upon facialimages. Flow diagram 1600 represents block 1145 and thus begins fromblock 1140 of the previously described FIG. 11 . The processes of flowdiagram 1600 may be executed on user detached monitor device 120 and/ora combination of user detached monitor device 120 and one of centralmonitoring system 2000 or central monitoring system 2100. Following flowdiagram 1600, a message is displayed to the monitored individual usingvisual display and touch screen 116 of user detached monitor device 120(block 1605). The message requests the monitored individual to record avideo of their face using forward camera 172. A still image of themonitored individual's face is recorded as a recorded face image.

It is determined whether the monitored individual has completedrecording a video of their face (block 1610). Where a recorded faceimage is not yet available (block 1610), it is determined whether atimeout condition has been met (block 1615). A monitored individual isgiven a defined time period to comply with the request, and after thedefined time period has passed the timeout condition is met. If thetimeout condition has been met (block 1615), a timeout error isindicated (block 1620) and the processing returns to block 1150 of FIG.11 without rendering an analysis of whether the monitored individual isimpaired by alcohol.

Alternatively, where it is determined that the recorded face image isavailable (block 1610), an anomaly detection is performed on therecorded face image (block 1630). Such anomaly detection may beperformed by any machine learning process designed to detect anomalouselements of an individual's face. As such, the anomaly detection isperformed by a machine learning model that has been trained with anumber of instances of the monitored individual's face image that wererecorded when the monitored individual was not impaired. In someembodiments, such voice data is collected as discussed above in relationto FIG. 6 . Based upon the disclosure provided herein, one of ordinaryskill in the art will recognize a variety of types of machine learningmodels that may be trained to perform voice anomaly detection inrelation to different embodiments.

Where an anomaly is not detected in the monitored individual's faceimage (i.e., the monitored individual appears the same as they alwaysappear) (block 1635), no anomaly is indicated (block 1640) and noimpairment is indicated or reported (block 1665). At this juncture, theprocessing returns to block 1150 of FIG. 11 without rendering ananalysis of whether the monitored individual is impaired.

Alternatively, where an anomaly is detected (i.e., the monitoredindividual appears different from the way they always appear) (block1635), a facial impairment model is applied to the recorded message toyield an indication of whether the monitored individual is impaired by,for example, drugs or alcohol (block 1645). The facial impairment modelmay be implemented in, for example, facial image based classificationengine 2140 or visual based classification engine 199 depending upon theparticular implementation. As discussed above in relation to Fig. thefacial impairment model is trained using a number of previouslyclassified face images. The facial impairment data may be derived from alarge number of different monitored individuals, and have beenclassified as either indicating impairment or not. In some cases theclassification is based upon a user input 2002 of central monitoringsystem 2000 or user input 2102 of central monitoring system 2100. Inother cases, the classification is based upon the output from the facialimpairment model. In some embodiments, the facial impairment model is aTensorFlow™ backbone used to generate a model that can be exported to aselected platform. Based upon the disclosure provided herein, one ofordinary skill in the art will recognize a variety of machine learningmodel types that may be trained using facial image data from testedindividuals to determine whether a newly recorded message shows whetheran individual is impaired or not.

The output from the facial impairment model indicates a likelihood thatthe monitored individual is impaired based upon features in the stillface image received from the monitored individual. It is determinedwhether the likelihood value received from the facial impairment modelis higher than a high probability value (block 1650). Such a highprobability value is selected as high enough to warrant indicatingimpairment without having a human user consider the recently receivedface image from the monitored individual. In some embodiments the highprobability value is user programmable. Where the likelihood valuereceived from the facial impairment model is higher than the highprobability value (block 1650), impairment of the monitored individualis indicated and reported (block 1655) and the processing returns toblock 1150 of FIG. 11 .

Where, on the other hand, the likelihood value received from the facialimpairment model is not higher than the high probability value (block1650), it is determined whether the likelihood value received from thefacial impairment model is lower than a low probability value (block1660). Such a low probability value is selected as low enough to warrantindicating no impairment without having a human user consider therecently received face image. In some embodiments the low probabilityvalue is user programmable. Where the likelihood value received from thefacial impairment model is lower than the low probability value (block1660), no impairment is indicated or reported (block 1665) and theprocessing returns to block 1150 of FIG. 11 .

Alternatively, where the likelihood value received from the facialimpairment model is not lower than the low probability value (block1660), an ambiguous condition has occurred. In such a situation, therecently received face image is forwarded to a user capable ofclassifying the data as indicative of impairment or not (block 1670).The user may be, for example, a human responsible for making finaldeterminations of impairment based at least in part upon the recentlyreceived face image. In some cases, the human may be a supervisingauthority of the monitored individual. This process may forward the faceimage to a central monitoring system (e.g., central monitoring system2100) which is capable of presenting the data to a user and storing theface image and user classification together in a database. Where theuser indicates impairment (block 1675) the impairment is indicated andreported (block 1655), and the processing returns to block 1150 of FIG.11 . Alternatively, where the user indicates no impairment (block 1675),no impairment is indicated or reported (block 1665) and the processingreturns to block 1150 of FIG. 11 .

In some embodiments, where the likelihood value received from the drugimpairment model is not lower than the low probability value (block1660) indicating the aforementioned ambiguous condition has occurred, itis determined if an additional impairment test should be run (block1694). Where an additional impairment test is to be run (block 1694),one or more additional impairment tests are performed (block 1696). Theadditional impairment test(s) may include one or more of: an alcoholimpairment test similar to that discussed herein in relation to FIG. 12, a voice based impairment test similar to that discussed herein inrelation to FIG. 14 , a movement based impairment test similar to thatdiscussed herein in relation to FIG. 15 , and/or a drug based impairmenttest similar to that discussed herein in relation to FIGS. 17 .

Turning to FIGS. 17 a-17 b , flow diagram 1700 and flow diagram 1780together show a method in accordance with various embodiments fordetermining impairment based upon breath VOC measurements. Flow diagram1700 and flow diagram 1780 together represent block 1155 and thus beginsfrom block 1150 of the previously described FIG. 11 . The processes offlow diagram 1700 and flow diagram 1780 may be executed on one of userdetached monitor device 120 or breath based impairment detection device192 depending upon the system implementation, and/or a combination ofone of user detached monitor device 120 or breath based impairmentdetection device 192 and one of central monitoring system 2000 orcentral monitoring system 2100. Turning to FIG. 17 a and following flowdiagram 1700, a request is made for the monitored individual to blowinto breath tube 190 while taking a video using one of forward camera172 or reverse camera 173 of user detached monitor device including themonitored individual's face and surrounding region while blowing (block1705). This request may be provided via a display on either or both ofuser detached monitor device 120 and/or breath based impairmentdetection device 192.

A sensor on breath based impairment detection device 192 detects whetherat least a minimum gas flow is reported from breath tube 190 (block1710). This sensing may be done using any sensor known in the art fordetecting gas flow volume. The sensor may be included as part of breathsensor 166 in breath based impairment detection device 192, with theinformation from the sensor being provided to one or both of alcoholimpairment classification engine 168 and/or drug impairmentclassification engine 169. Where it is determined that insufficient gasflow has been detected by the sensor (block 1710), it is determinedwhether a timeout condition has been met (block 1715). A monitoredindividual is given a defined time period to comply with the request,and after the defined time period has passed the timeout condition ismet. If the timeout condition has been met (block 1720), a timeout erroris indicated (block 1720) and the processing returns to block 1160 ofFIG. 11 without rendering an analysis of whether the monitoredindividual is impaired by drugs.

Alternatively, where it is determined that sufficient gas flow has beendetected by the sensor (block 1710), a still image from the video of themonitored individual's face and surrounding area is isolated (block1725). An interference classification model is applied to the stillimage to yield an indication of whether the monitored individual isinterfering with breath tube (block 1730). As discussed above inrelation to FIG. 3 , the interference classification model is trainedusing a number of previously classified images showing a monitoredindividual while they are breathing into breath tube 190. The images maybe derived from a large number of different monitored individuals, andhave been classified as either indicating interference with breath tube190 or no interference with breath tube 190. In some cases theclassification is based upon a user input 2002 of central monitoringsystem 2000 or user input 2102 of central monitoring system 2100. Inother cases, the classification is based upon the output from the userclassification model. FIG. 13 a are drawings representing a number ofexample still images 1302, 1304, 1306, 1308, 1310 each showing anrespective individual with a breath tube properly inserted in theirmouth while breathing, and without any addition elements included in theimage that appear to be interfering with the test. FIG. 13 b aredrawings representing a number of example still images 1352, 1354, 1356,1358 each showing an respective individual interfering with a breathtube. Still image 1352 shows an individual with both the breath tube anda secondary tube inserted into their mouth. In such an approach, a gascan be blown into the individual's mouth that flows out through thebreath tube simulating breath. Still image 1354 shows an individual withthe breath tube inserted in their mouth, and a secondary tube connectedto into the breath tube. In such an approach, a gas can be blown fromthe secondary tube into the breath tube simulating breath. Still image1356 shows an individual with the breath tube inserted in their mouth,but their hand is over their mouth and their head is turned potentiallyhiding a secondary tube. Still image 1356 shows an individual with thebreath tube near their mouth along with a secondary tube. In such anapproach, a gas can be blown toward the individual's mouth from thesecondary tube and into the breath tube simulating breath.

In some embodiments, the interference classification model is aTensorFlow™ backbone used to generate a model that can be exported to aselected platform. Based upon the disclosure provided herein, one ofordinary skill in the art will recognize a variety of machine learningmodel types that may be trained using images of individuals blowing in abreath tube to determine whether a newly received image shows anindividual properly using the breath tube.

The output from the interference classification model indicates alikelihood that the monitored individual is interfering with breath tube190 or not using it properly. It is determined whether the likelihoodvalue received from the interference classification model is higher thana high probability value (block 1735). Such a high probability value isselected as high enough to warrant indicating interference withouthaving a human user consider the image. In some embodiments the highprobability value is user programmable. Where the likelihood valuereceived from the interference classification model is higher than thehigh probability value (block 1735), an interference is indicated (block1740) and the processing returns to block 1160 of FIG. 11 withoutrendering an analysis of whether the monitored individual is impaired bydrugs.

Where, on the other hand, the likelihood value received from theinterference classification model is not higher than the highprobability value (block 1735), it is determined whether the likelihoodvalue received from the interference classification model is lower thana low probability value (block 1750). Such a low probability value isselected as low enough to warrant indicating no interference withouthaving a human user consider the image. In some embodiments the lowprobability value is user programmable. Where the likelihood valuereceived from the interference classification model is lower than thelow probability value (block 1750), no interference is indicated (block1765). In this situation, drug impairment testing applied to thereceived breath sample (block 1770). Block 1770 is shown in dashed linesas it is depicted in more detail in flow diagram 1780 of FIG. 17 b.

Turning to FIG. 17 b and following flow diagram 1780, a drug impairmentmodel is applied to the breath data received from the sensor to yield anindication of whether the monitored individual is impaired by drugs(block 1782). The received breath data includes a type quantity of VOCsfound in the monitored individual's breath sample. The drug impairmentmodel may be implemented in, for example, breath drug basedclassification engine 2150 or drug impairment classification engine 169depending upon the particular implementation. As discussed above inrelation to FIG. 4 , the drug impairment model is trained using a numberof previously classified breath data sets corresponding to monitoredindividuals. The breath data sets may be derived from a large number ofdifferent monitored individuals, and have been classified as eitherindicating drug impairment or not. In some cases the classification isbased upon a user input 2002 of central monitoring system 2000 or userinput 2102 of central monitoring system 2100. In other cases, theclassification is based upon the output from the drug impairment model.In some embodiments, the drug impairment model is a TensorFlow™ backboneused to generate a model that can be exported to a selected platform.Based upon the disclosure provided herein, one of ordinary skill in theart will recognize a variety of machine learning model types that may betrained using breath data sets sampled from the breath of individualsblowing in a breath tube to determine whether a newly received breathdata set shows whether an individual is drug impaired or not.

The output from the drug impairment model indicates a likelihood thatthe monitored individual is drug impaired based upon VOCs in the breathdata derived from the monitored individual. It is determined whether thelikelihood value received from the drug impairment model is higher thana high probability value (block 1784). Such a high probability value isselected as high enough to warrant indicating drug impairment withouthaving a human user consider the recently received breath data set fromthe monitored individual. In some embodiments the high probability valueis user programmable. Where the likelihood value received from the drugimpairment model is higher than the high probability value (block 1784),drug impairment of the monitored individual is indicated (block 1792)and the processing returns to block 1775 of FIG. 17 a where theindication is used and/or reported.

Where, on the other hand, the likelihood value received from the drugimpairment model is not higher than the high probability value (block1784), it is determined whether the likelihood value received from thedrug impairment model is lower than a low probability value (block1786). Such a low probability value is selected as low enough to warrantindicating no drug impairment without having a human user consider thebreath data set. In some embodiments the low probability value is userprogrammable. Where the likelihood value received from the drugimpairment model is lower than the low probability value (block 1786),no drug impairment is indicated (block 1788) and the processing returnsto block 1775 of FIG. 17 a where the indication is used and/or reported.

Alternatively, where the likelihood value received from the drugimpairment model is not lower than the low probability value (block1786), an ambiguous condition has occurred. In such a situation, therecently received breath data is forwarded to a user capable ofclassifying the data as indicative of drug impairment or not (block1789). The user may be, for example, a human responsible for makingfinal determinations of drug impairment based at least in part upon theVOC data. In some cases, the human may be a supervising authority of themonitored individual. This process may forward the breath to a centralmonitoring system (e.g., central monitoring system 2100) which iscapable of presenting the data to a user and storing the breath data anduser classification together in a database. Where the user indicatesimpairment (block 1790) the impairment is indicated (block 1792) and theprocessing returns to block 1775 of FIG. 17 a where the indication isused and/or reported. Alternatively, where the user indicates noimpairment (block 1790), no drug impairment is indicated (block 1788)and the processing returns to block 1775 of FIG. 17 a where theindication is used and/or reported.

In some embodiments, where the likelihood value received from the drugimpairment model is not lower than the low probability value (block1786) indicating the aforementioned ambiguous condition has occurred, itis determined if an additional impairment test should be run (block1794). Where an additional impairment test is to be run (block 1794),one or more additional impairment tests are performed (block 1796). Theadditional impairment test(s) may include one or more of: a voice basedimpairment test similar to that discussed herein in relation to FIG. 14, a movement based impairment test similar to that discussed herein inrelation to FIG. 14 , a facial image based impairment test similar tothat discussed herein in relation to FIG. 16 , or a breath alcohol basedimpairment test similar to that discussed herein in relation to FIG. 12. Returning to FIG. 17 a , the received impairment results (block 1792or block 1788) are reported (block 1775). Having reported the impairmentresults, the process then returns to block 1160 of FIG. 11 .

Turning to FIG. 18 , a flow diagram 1800 shows a method in accordancewith some embodiments for applying a multi-predictor machine learningmodel that is configured to yield an impairment classification basedupon two or more different types of data provided as respectivepredictors to the multi-predictor machine learning model. Flow diagram1800 represents block 1165 and thus begins from block 1160 of thepreviously described FIG. 11 . The processes of flow diagram 1800 may beexecuted on user detached monitor device 120 and/or a combination ofuser detached monitor device 120 and one of central monitoring system2000 or central monitoring system 2100. The processes rely upon amulti-predictor machine learning model that may be implemented, forexample, as part of multi-predictor classification engine 2160, ormulti-predictor classification engine 2050.

Following flow diagram 1800, a combination of two or more types ofpredictors are provided to a multi-predictor machine learning model(block 1830). Any of the two or more types of predictors may bythemselves be useful in classifying whether an individual is impaired,but the two or more are used together in the multi-predictor machinelearning model to enhance the accuracy of the classification ofimpairment or non-impairment. As just some examples, the two or moretypes of predictors may include two or more of: an alcohol basedimpairment result (e.g., an impairment result reported as part of block1275 of FIG. 12 ), a drug based impairment result (e.g., an impairmentresult reported as part of block 1788 or block 1792 of FIG. 17 b ),breath data (e.g., the breath data used in relation to block 1782 ofFIG. 17 b ), a voice based impairment result (e.g., an impairment resultreported as part of either block 1455 or block 1465 of FIG. 14 ), voicedata (e.g., the recorded message discussed in relation to block 1405 ofFIG. 14 ), a movement based impairment result (e.g., an impairmentresult reported as part of either block 1580 or block 1575 of FIG. 15 ),movement data (e.g., the recorded movement data discussed in relation toblock 1505 of FIG. 15 ), a facial image based impairment result (e.g.,an impairment result reported as part of either block 1655 or block 1665of FIG. 16 ), a facial image (e.g., the facial image discussed inrelation to block 1605 of FIG. 16 ). The processing includes applying amachine learning model to the combination of the two or more predictorsto yield a likelihood that an individual is impaired.

It is determined whether the likelihood value received from themulti-predictor machine learning model is higher than a high probabilityvalue (block 1850). Such a high probability value is selected as highenough to warrant indicating impairment without having a human userconsider any of the predictors. In some embodiments the high probabilityvalue is user programmable. Where the likelihood value received from themulti-predictor machine learning model is higher than the highprobability value (block 1850), impairment of the monitored individualis indicated and reported (block 1855) and the processing returns toblock 1105 of FIG. 11 .

Where, on the other hand, the likelihood value received from themulti-predictor machine learning model is not higher than the highprobability value (block 1950), it is determined whether the likelihoodvalue received from the multi-predictor machine learning model is lowerthan a low probability value (block 1860). Such a low probability valueis selected as low enough to warrant indicating no impairment withouthaving a human user consider the recently received predictors. In someembodiments the low probability value is user programmable. Where thelikelihood value received from the multi-predictor machine learningmodel is lower than the low probability value (block 1860), noimpairment is indicated or reported (block 1865) and the processingreturns to block 1105 of FIG. 11 .

Alternatively, where the likelihood value received from themulti-predictor machine learning model is not lower than the lowprobability value (block 1860), an ambiguous condition has occurred. Insuch a situation, the recently received predictors are forwarded to auser capable of classifying the data as indicative of impairment or not(block 1870). The user may be, for example, a human responsible formaking final determinations of impairment based at least in part uponthe recently received face image. In some cases, the human may be asupervising authority of the monitored individual. This process mayforward the face image to a central monitoring system (e.g., centralmonitoring system 2100) which is capable of presenting the data to auser and storing the predictors and user classification together in adatabase. Where the user indicates impairment (block 1875) theimpairment is indicated and reported (block 1855), and the processingreturns to block 1105 of FIG. 11 . Alternatively, where the userindicates no impairment (block 1875), no impairment is indicated orreported (block 1865) and the processing returns to block 1105 of FIG.11 .

In conclusion, the present invention provides for novel systems,devices, and methods for monitoring individuals. While detaileddescriptions of one or more embodiments of the invention have been givenabove, various alternatives, modifications, and equivalents will beapparent to those skilled in the art without varying from the spirit ofthe invention. Therefore, the above description should not be taken aslimiting the scope of the invention, which is defined by the appendedclaims.

What is claimed is:
 1. A system for detecting impairment based uponmovement, the system comprising: a movement sensor configured to receivemovement information about a user detached monitor device; one or moreprocessors; a non-transient computer readable medium coupled to the oneor more processors, and having stored therein instructions which whenexecuted by the one or more processors, causes the one or moreprocessors to: receive the movement information from the movementsensor; apply a movement impairment model to the movement information toyield a probability that the individual is impaired; indicate alikelihood of impairment based at least in part on a determination thatthe probability exceeds a first threshold; and indicate no impairmentwhen the probability is less than a second threshold.
 2. The system ofclaim 1, the system further comprising: a camera; wherein thenon-transient computer readable medium further having stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: receive an image of surroundings of theindividual; and based upon the image showing one or more physicalsupports around the individual, cause a request for the individual tomove to another location.
 3. The system of claim 1, the system furthercomprising: a camera; wherein the non-transient computer readable mediumfurther having stored therein instructions which when executed by theone or more processors, causes the one or more processors to: receive animage of surroundings of the individual; and wherein indicating noimpairment is based at least in part on the image showing the individuallocated away from a physical support.
 4. The system of claim 1, thesystem further comprising: a camera; a display; wherein thenon-transient computer readable medium further having stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: receive a face image of the individualindicating the individual is watching the display; cause a video streamto play on the display; and wherein indicating no impairment is based atleast in part on the face image of the individual indicating theindividual is watching the display.
 5. The system of claim 1, whereinthe non-transient computer readable medium further having stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: compare the movement information with amovement threshold; and wherein indicating no impairment is based atleast in part on the movement information being greater than themovement threshold.
 6. The system of claim 1, wherein the movementimpairment model is a machine learning model trained using at least onehundred instances of movement information data.
 7. The system of claim6, wherein the at least one hundred instances of movement informationcorrespond to at least ten different individuals undergoing a movementbased impairment test.
 8. The system of claim 1, wherein thenon-transient computer readable medium further having stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: cause a request to be sent to theindividual to perform an additional impairment test.
 9. The system ofclaim 8, wherein the additional impairment test is selected from a groupconsisting of: a facial image based impairment test, and a voice basedimpairment test.
 10. The system of claim 1, wherein the non-transientcomputer readable medium further having stored therein instructionswhich when executed by the one or more processors, causes the one ormore processors to: forward the movement information to a user forclassification when the probability is both less than the firstthreshold and greater than the second threshold.
 11. The system of claim1, wherein the non-transient computer readable medium further havingstored therein instructions which when executed by the one or moreprocessors, causes the one or more processors to: report the likelihoodof impairment to a recipient device apart from the one or moreprocessors.
 12. A method for detecting impairment based upon movementinformation, the method comprising: receiving, by a processor, movementinformation from a movement sensor included in a user detached monitordevice; applying, by the processor, a movement impairment model to themovement information to yield a probability that the individual isimpaired; indicating, by the processor, a likelihood of impairment basedat least in part on a determination that the probability exceeds a firstthreshold; and indicating, by the processor, no impairment when theprobability is less than a second threshold.
 13. The method of claim 12,the method further comprising: receiving, by the processor, an image ofsurroundings of the individual from a camera; and based upon the imageshowing one or more physical supports around the individual, causing, bythe processor, a request for the individual to move to another location.14. The method of claim 12, the system further comprising: receiving, bythe processor, an image of surroundings of the individual; and whereinindicating no impairment is based at least in part on the image showingthe individual located away from a physical support.
 15. The method ofclaim 12, the system further comprising: receiving, by the processor, aface image of the individual indicating the individual is watching thedisplay; causing, by the processor, a video stream to play on thedisplay; and wherein indicating no impairment is based at least in parton the face image of the individual indicating the individual iswatching the display.
 16. The method of claim 12, wherein thenon-transient computer readable medium further having stored thereininstructions which when executed by the one or more processors, causesthe one or more processors to: comparing, by the processor, the movementinformation with a movement threshold; and wherein indicating noimpairment is based at least in part on the movement information beinggreater than the movement threshold.
 17. The method of claim 12, whereinthe movement impairment model is a machine learning model trained usingat least one hundred instances of movement information data.
 18. Themethod of claim 17, wherein the at least one hundred instances ofmovement information correspond to at least ten different individualsundergoing a movement based impairment test.
 19. The method of claim 12,the method further comprising: causing a request to be sent to theindividual to perform an additional impairment test, wherein theadditional impairment test is selected from a group consisting of: afacial image based impairment test, and a voice based impairment test.20. A non-transient computer readable medium having stored thereininstructions, which when executed by a hardware processing system causethe hardware processing system to: receive movement information from amovement sensor; apply a movement impairment model to the movementinformation to yield a probability that the individual is impaired,wherein the movement impairment model is a machine learning modeltrained using at least one hundred instances of movement informationdata, and wherein the at least one hundred instances of movementinformation correspond to at least ten different individuals undergoinga movement based impairment test; indicate a likelihood of impairmentbased at least in part on a determination that the probability exceeds afirst threshold; and indicate no impairment when the probability is lessthan a second threshold.